Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, and A. Adibi, “Knowledge discovery in nanophotonics using geometric deep learning,” Adv. Intell. Syst. 2, 1900132 (2020).
[Crossref]
Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theor. Simul. 2, 1900088 (2019).
[Crossref]
Y. Kiarashinejad, S. Abdollahramezani, and A. Adibi, “Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures,” arXiv:1902.03865 (2019).
E. Ashalley, K. Acheampong, L. V. Besteiro, L. V. Besteiro, P. Yu, A. Neogi, A. O. Govorov, A. O. Govorov, and Z. M. Wang, “Multitask deep-learning-based design of chiral plasmonic metamaterials,” Photon. Res. 8, 1213–1225 (2020).
[Crossref]
C. L. Cortes, S. Adhikari, X. Ma, and S. K. Gray, “Accelerating quantum optics experiments with statistical learning,” Appl. Phys. Lett. 116, 184003 (2020).
[Crossref]
Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, and A. Adibi, “Knowledge discovery in nanophotonics using geometric deep learning,” Adv. Intell. Syst. 2, 1900132 (2020).
[Crossref]
Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theor. Simul. 2, 1900088 (2019).
[Crossref]
Y. Kiarashinejad, S. Abdollahramezani, and A. Adibi, “Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures,” arXiv:1902.03865 (2019).
B. Midtvedt, E. Olsén, F. Eklund, F. Höök, C. B. Adiels, G. Volpe, and D. Midtvedt, “Holographic characterisation of subwavelength particles enhanced by deep learning,” arXiv:2006.11154 (2020).
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
E. Nehme, D. Freedman, R. Gordon, B. Ferdman, L. E. Weiss, O. Alalouf, T. Naor, R. Orange, T. Michaeli, and Y. Shechtman, “DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning,” Nat. Methods 17, 734–740 (2020).
[Crossref]
M. Elzouka, C. Yang, A. Albert, S. Lubner, and R. S. Prasher, “Interpretable inverse design of particle spectral emissivity using machine learning,” arXiv:2002.04223 (2020).
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, inception-ResNet and the impact of residual connections on learning,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (2016), pp. 4278–4284.
F. Zangeneh-Nejad, D. L. Sounas, A. Alù, and R. Fleury, “Analogue computing with metamaterials,” Nat. Rev. Mater. 6, 207–225 (2021).
[Crossref]
Y.-T. Luo, P.-Q. Li, D.-T. Li, Y.-G. Peng, Z.-G. Geng, S.-H. Xie, Y. Li, A. Alù, J. Zhu, and X.-F. Zhu, “Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures,” Research 2020, 8757403 (2020).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
S. An, B. Zheng, H. Tang, M. Y. Shalaginov, L. Zhou, H. Li, T. Gu, J. Hu, C. Fowler, and H. Zhang, “Multifunctional metasurface design with a generative adversarial network,” arXiv:1908.04851 (2020).
P. R. Wiecha, P. R. Wiecha, C. Majorel, C. Girard, A. Cuche, V. Paillard, O. L. Muskens, and A. Arbouet, “Design of plasmonic directional antennas via evolutionary optimization,” Opt. Express 27, 29069–29081 (2019).
[Crossref]
P. R. Wiecha, A. Arbouet, C. Girard, A. Lecestre, G. Larrieu, and V. Paillard, “Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas,” Nat. Nanotechnol. 12, 163–169 (2017).
[Crossref]
L.-J. Black, Y. Wang, C. H. de Groot, A. Arbouet, and O. L. Muskens, “Optimal polarization conversion in coupled dimer plasmonic nanoantennas for metasurfaces,” ACS Nano 8, 6390–6399 (2014).
[Crossref]
A. Argun, T. Thalheim, S. Bo, F. Cichos, and G. Volpe, “Enhanced force-field calibration via machine learning,” Appl. Phys. Rev. 7, 041404 (2020).
[Crossref]
S. Helgadottir, A. Argun, and G. Volpe, “Digital video microscopy enhanced by deep learning,” Optica 6, 506–513 (2019).
[Crossref]
I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via deep learning,” Light Sci. Appl. 7, 60 (2018).
[Crossref]
W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36, 460–468 (2018).
[Crossref]
T. Asano and S. Noda, “Iterative optimization of photonic crystal nanocavity designs by using deep neural networks,” Nanophotonics 8, 2243–2256 (2019).
[Crossref]
E. A. Ash and G. Nicholls, “Super-resolution aperture scanning microscope,” Nature 237, 510–512 (1972).
[Crossref]
E. Ashalley, K. Acheampong, L. V. Besteiro, L. V. Besteiro, P. Yu, A. Neogi, A. O. Govorov, A. O. Govorov, and Z. M. Wang, “Multitask deep-learning-based design of chiral plasmonic metamaterials,” Photon. Res. 8, 1213–1225 (2020).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
Y. Augenstein and C. Rockstuhl, “Inverse design of nanophotonic devices with structural integrity,” ACS Photonics 7, 2190–2196 (2020).
[Crossref]
S. So, T. Badloe, J. Noh, J. Bravo-Abad, and J. Rho, “Deep learning enabled inverse design in nanophotonics,” Nanophotonics 9, 1041–1057 (2020).
[Crossref]
T. Badloe, I. Kim, and J. Rho, “Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning,” Phys. Chem. Chem. Phys. 22, 2337–2342 (2020).
[Crossref]
G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett, “Deep learning techniques for inverse problems in imaging,” IEEE J. Sel. Areas Inform. Theor. 1, 39–56 (2020).
[Crossref]
G. C. des Francs, J. Barthes, A. Bouhelier, J. C. Weeber, A. Dereux, A. Cuche, and C. Girard, “Plasmonic Purcell factor and coupling efficiency to surface plasmons. Implications for addressing and controlling optical nanosources,” J. Opt. 18, 094005 (2016).
[Crossref]
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
R. Selle, T. Brixner, T. Bayer, M. Wollenhaupt, and T. Baumert, “Modelling of ultrafast coherent strong-field dynamics in potassium with neural networks,” J. Phys. B 41, 074019 (2008).
[Crossref]
R. Selle, T. Brixner, T. Bayer, M. Wollenhaupt, and T. Baumert, “Modelling of ultrafast coherent strong-field dynamics in potassium with neural networks,” J. Phys. B 41, 074019 (2008).
[Crossref]
J. M. Ede and R. Beanland, “Partial scanning transmission electron microscopy with deep learning,” Sci. Rep. 10, 8332 (2020).
[Crossref]
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).
[Crossref]
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT, 2016).
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
E. Ashalley, K. Acheampong, L. V. Besteiro, L. V. Besteiro, P. Yu, A. Neogi, A. O. Govorov, A. O. Govorov, and Z. M. Wang, “Multitask deep-learning-based design of chiral plasmonic metamaterials,” Photon. Res. 8, 1213–1225 (2020).
[Crossref]
E. Ashalley, K. Acheampong, L. V. Besteiro, L. V. Besteiro, P. Yu, A. Neogi, A. O. Govorov, A. O. Govorov, and Z. M. Wang, “Multitask deep-learning-based design of chiral plasmonic metamaterials,” Photon. Res. 8, 1213–1225 (2020).
[Crossref]
C. You, M. A. Quiroz-Juárez, A. Lambert, N. Bhusal, C. Dong, A. Perez-Leija, A. Javaid, R. de. J. León-Montiel, and O. S. Magaña-Loaiza, “Identification of light sources using machine learning,” Appl. Phys. Rev. 7, 021404 (2020).
[Crossref]
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
A. M. Palmieri, E. Kovlakov, F. Bianchi, D. Yudin, S. Straupe, J. D. Biamonte, and S. Kulik, “Experimental neural network enhanced quantum tomography,” npj Quantum Inf. 6, 20 (2020).
[Crossref]
A. M. Palmieri, E. Kovlakov, F. Bianchi, D. Yudin, S. Straupe, J. D. Biamonte, and S. Kulik, “Experimental neural network enhanced quantum tomography,” npj Quantum Inf. 6, 20 (2020).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
M. Närhi, L. Salmela, J. Toivonen, C. Billet, J. M. Dudley, and G. Genty, “Machine learning analysis of extreme events in optical fibre modulation instability,” Nat. Commun. 9, 4923 (2018).
[Crossref]
L.-J. Black, Y. Wang, C. H. de Groot, A. Arbouet, and O. L. Muskens, “Optimal polarization conversion in coupled dimer plasmonic nanoantennas for metasurfaces,” ACS Nano 8, 6390–6399 (2014).
[Crossref]
A. K. González-Alcalde, R. Salas-Montiel, V. Kalt, S. Blaize, and D. Macías, “Engineering colors in all-dielectric metasurfaces: metamodeling approach,” Opt. Lett. 45, 89–92 (2020).
[Crossref]
V. Kalt, A. K. González-Alcalde, S. Es-Saidi, R. Salas-Montiel, S. Blaize, and D. Macías, “Metamodeling of high-contrast-index gratings for color reproduction,” J. Opt. Soc. Am. A 36, 79–88 (2019).
[Crossref]
A. Argun, T. Thalheim, S. Bo, F. Cichos, and G. Volpe, “Enhanced force-field calibration via machine learning,” Appl. Phys. Rev. 7, 041404 (2020).
[Crossref]
S. Popoff, G. Lerosey, M. Fink, A. C. Boccara, and S. Gigan, “Image transmission through an opaque material,” Nat. Commun. 1, 81 (2010).
[Crossref]
Z. A. Kudyshev, S. I. Bogdanov, T. Isacsson, A. V. Kildishev, A. Boltasseva, and V. M. Shalaev, “Rapid classification of quantum sources enabled by machine learning,” Adv. Quantum Technol. 3, 2000067 (2020).
[Crossref]
Z. A. Kudyshev, S. I. Bogdanov, T. Isacsson, A. V. Kildishev, A. Boltasseva, and V. M. Shalaev, “Rapid classification of quantum sources enabled by machine learning,” Adv. Quantum Technol. 3, 2000067 (2020).
[Crossref]
W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15, 77–90 (2020).
[Crossref]
Z. A. Kudyshev, A. V. Kildishev, V. M. Shalaev, and A. Boltasseva, “Machine learning assisted global optimization of photonic devices,” Nanophotonics 10, 371–383 (2020).
[Crossref]
G. C. des Francs, J. Barthes, A. Bouhelier, J. C. Weeber, A. Dereux, A. Cuche, and C. Girard, “Plasmonic Purcell factor and coupling efficiency to surface plasmons. Implications for addressing and controlling optical nanosources,” J. Opt. 18, 094005 (2016).
[Crossref]
S. So, T. Badloe, J. Noh, J. Bravo-Abad, and J. Rho, “Deep learning enabled inverse design in nanophotonics,” Nanophotonics 9, 1041–1057 (2020).
[Crossref]
A. A. Melnikov, H. P. Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, and H. J. Briegel, “Active learning machine learns to create new quantum experiments,” Proc. Natl. Acad. Sci. USA 115, 1221–1226 (2018).
[Crossref]
R. Selle, T. Brixner, T. Bayer, M. Wollenhaupt, and T. Baumert, “Modelling of ultrafast coherent strong-field dynamics in potassium with neural networks,” J. Phys. B 41, 074019 (2008).
[Crossref]
R. Selle, G. Vogt, T. Brixner, G. Gerber, R. Metzler, and W. Kinzel, “Modeling of light-matter interactions with neural networks,” Phys. Rev. A 76, 023810 (2007).
[Crossref]
A. I. Kuznetsov, A. E. Miroshnichenko, M. L. Brongersma, Y. S. Kivshar, and B. Luk’yanchuk, “Optically resonant dielectric nanostructures,” Science 354, aag2472 (2016).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” arXiv:1505.04597 (2015).
J. Moughames, X. Porte, M. Thiel, G. Ulliac, L. Larger, M. Jacquot, M. Kadic, and D. Brunner, “Three-dimensional waveguide interconnects for scalable integration of photonic neural networks,” Optica 7, 640–646 (2020).
[Crossref]
X. Porte, A. Skalli, N. Haghighi, S. Reitzenstein, J. A. Lott, and D. Brunner, “A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser,” arXiv:2012.11153 (2020).
T. Baumeister, S. L. Brunton, and J. N. Kutz, “Deep learning and model predictive control for self-tuning mode-locked lasers,” J. Opt. Soc. Am. B 35, 617–626 (2018).
[Crossref]
J. N. Kutz and S. L. Brunton, “Intelligent systems for stabilizing mode-locked lasers and frequency combs: machine learning and equation-free control paradigms for self-tuning optics,” Nanophotonics 4, 459–471 (2015).
[Crossref]
S. L. Brunton, X. Fu, and J. N. Kutz, “Self-tuning fiber lasers,” IEEE J. Sel. Top. Quantum Electron. 20, 464–471 (2014).
[Crossref]
M. V. Zhelyeznyakov, S. L. Brunton, and A. Majumdar, “Deep learning to accelerate Maxwell’s equations for inverse design of dielectric metasurfaces,” arXiv:2008.10632 (2020).
J. Zhou, B. Huang, Z. Yan, and J.-C. G. Bünzli, “Emerging role of machine learning in light-matter interaction,” Light Sci. Appl. 8, 1 (2019).
[Crossref]
D. Z. Zhu, E. B. Whiting, S. D. Campbell, D. B. Burckel, and D. H. Werner, “Optimal high efficiency 3D plasmonic metasurface elements revealed by lazy ants,” ACS Photonics 6, 2741–2748 (2019).
[Crossref]
B. Gallinet, J. Butet, and O. J. F. Martin, “Numerical methods for nanophotonics: standard problems and future challenges,” Laser Photonics Rev. 9, 577–603 (2015).
[Crossref]
W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15, 77–90 (2020).
[Crossref]
Z. Liu, Z. Liu, Z. Zhu, and W. Cai, “Topological encoding method for data-driven photonics inverse design,” Opt. Express 28, 4825–4835 (2020).
[Crossref]
Z. Liu, L. Raju, D. Zhu, and W. Cai, “A hybrid strategy for the discovery and design of photonic structures,” IEEE J. Emerging Sel. Top. Circuits Syst. 10, 126–135 (2020).
[Crossref]
Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18, 6570–6576 (2018).
[Crossref]
Y. Li, Y. Wang, S. Qi, Q. Ren, L. Kang, S. D. Campbell, P. L. Werner, and D. H. Werner, “Predicting scattering from complex nano-structures via deep learning,” IEEE Access 8, 139983 (2020).
[Crossref]
D. Z. Zhu, E. B. Whiting, S. D. Campbell, D. B. Burckel, and D. H. Werner, “Optimal high efficiency 3D plasmonic metasurface elements revealed by lazy ants,” ACS Photonics 6, 2741–2748 (2019).
[Crossref]
S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, and D. H. Werner, “Review of numerical optimization techniques for meta-device design [Invited],” Opt. Mater. Express 9, 1842–1863 (2019).
[Crossref]
S. D. Campbell, D. Z. Zhu, E. B. Whiting, J. Nagar, D. H. Werner, and P. L. Werner, “Advanced multi-objective and surrogate-assisted optimization of topologically diverse metasurface architectures,” Proc. SPIE 10719, 107190U (2018).
[Crossref]
B. Wang, J. C. Cancilla, J. S. Torrecilla, and H. Haick, “Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase,” Nano Lett. 14, 933–938 (2014).
[Crossref]
J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4, eaar4206 (2018).
[Crossref]
S. Wang, K. Fan, N. Luo, Y. Cao, F. Wu, C. Zhang, K. A. Heller, and L. You, “Massive computational acceleration by using neural networks to emulate mechanism-based biological models,” Nat. Commun. 10, 4354 (2019).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydn, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2018).
[Crossref]
S. Chan and E. L. Siegel, “Will machine learning end the viability of radiology as a thriving medical specialty?” Br. J. Radiol. 92, 20180416 (2018).
[Crossref]
A. Youssry, R. J. Chapman, A. Peruzzo, C. Ferrie, and M. Tomamichel, “Modeling and control of a reconfigurable photonic circuit using deep learning,” Quantum Sci. Technol. 5, 025001 (2020).
[Crossref]
P. Zhang, S. Liu, A. Chaurasia, D. Ma, M. J. Mlodzianoski, E. Culurciello, and F. Huang, “Analyzing complex single-molecule emission patterns with deep learning,” Nat. Methods 15, 913–916 (2018).
[Crossref]
J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” in Advances in Neural Information Processing Systems, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, eds. (Curran Associates, 2012), Vol. 25, pp. 341–349.
G. D. Bruce, L. O’Donnell, M. Chen, M. Facchin, and K. Dholakia, “Femtometer-resolved simultaneous measurement of multiple laser wavelengths in a speckle wavemeter,” Opt. Lett. 45, 1926–1929 (2020).
[Crossref]
J. Jiang, M. Chen, and J. A. Fan, “Deep neural networks for the evaluation and design of photonic devices,” arXiv:2007.00084 (2020).
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
Y. Chen, L. Lu, G. E. Karniadakis, and L. D. Negro, “Physics-informed neural networks for inverse problems in nano-optics and metamaterials,” Opt Express 28, 11618–11633 (2020).
[Crossref]
B. Hu, B. Wu, D. Tan, J. Xu, J. Xu, Y. Chen, and Y. Chen, “Robust inverse-design of scattering spectrum in core-shell structure using modified denoising autoencoder neural network,” Opt. Express 27, 36276–36285 (2019).
[Crossref]
B. Hu, B. Wu, D. Tan, J. Xu, J. Xu, Y. Chen, and Y. Chen, “Robust inverse-design of scattering spectrum in core-shell structure using modified denoising autoencoder neural network,” Opt. Express 27, 36276–36285 (2019).
[Crossref]
W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, “Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy,” Adv. Mater. 31, 1901111 (2019).
[Crossref]
W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12, 6326–6334 (2018).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M.-H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]
T. Chugh, C. Sun, H. Wang, and Y. Jin, “Surrogate-assisted evolutionary optimization of large problems,” in High-Performance Simulation-Based Optimization, T. Bartz-Beielstein, B. Filipič, P. Korošec, and E.-G. Talbi, eds. (Springer, 2020), pp. 165–187.
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
A. Argun, T. Thalheim, S. Bo, F. Cichos, and G. Volpe, “Enhanced force-field calibration via machine learning,” Appl. Phys. Rev. 7, 041404 (2020).
[Crossref]
D. A. Cirovic, “Feed-forward artificial neural networks: applications to spectroscopy,” TRAC Trends Anal. Chem. 16, 148–155 (1997).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
C. L. Cortes, S. Adhikari, X. Ma, and S. K. Gray, “Accelerating quantum optics experiments with statistical learning,” Appl. Phys. Lett. 116, 184003 (2020).
[Crossref]
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT, 2016).
P. R. Wiecha, P. R. Wiecha, C. Majorel, C. Girard, A. Cuche, V. Paillard, O. L. Muskens, and A. Arbouet, “Design of plasmonic directional antennas via evolutionary optimization,” Opt. Express 27, 29069–29081 (2019).
[Crossref]
G. C. des Francs, J. Barthes, A. Bouhelier, J. C. Weeber, A. Dereux, A. Cuche, and C. Girard, “Plasmonic Purcell factor and coupling efficiency to surface plasmons. Implications for addressing and controlling optical nanosources,” J. Opt. 18, 094005 (2016).
[Crossref]
P. Zhang, S. Liu, A. Chaurasia, D. Ma, M. J. Mlodzianoski, E. Culurciello, and F. Huang, “Analyzing complex single-molecule emission patterns with deep learning,” Nat. Methods 15, 913–916 (2018).
[Crossref]
R. Pestourie, Y. Mroueh, T. V. Nguyen, P. Das, and S. G. Johnson, “Active learning of deep surrogates for PDEs: application to metasurface design,” arXiv:2008.12649 (2020).
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
L.-J. Black, Y. Wang, C. H. de Groot, A. Arbouet, and O. L. Muskens, “Optimal polarization conversion in coupled dimer plasmonic nanoantennas for metasurfaces,” ACS Nano 8, 6390–6399 (2014).
[Crossref]
K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms (Wiley, 2001), Vol. 16.
R. Iten, T. Metger, H. Wilming, L. del Rio, and R. Renner, “Discovering physical concepts with neural networks,” Phys. Rev. Lett. 124, 010508 (2020).
[Crossref]
J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4, eaar4206 (2018).
[Crossref]
D. W. Pohl, W. Denk, and M. Lanz, “Optical stethoscopy: image recording with resolution λ/20,” Appl. Phys. Lett. 44, 651–653 (1984).
[Crossref]
G. C. des Francs, J. Barthes, A. Bouhelier, J. C. Weeber, A. Dereux, A. Cuche, and C. Girard, “Plasmonic Purcell factor and coupling efficiency to surface plasmons. Implications for addressing and controlling optical nanosources,” J. Opt. 18, 094005 (2016).
[Crossref]
G. C. des Francs, J. Barthes, A. Bouhelier, J. C. Weeber, A. Dereux, A. Cuche, and C. Girard, “Plasmonic Purcell factor and coupling efficiency to surface plasmons. Implications for addressing and controlling optical nanosources,” J. Opt. 18, 094005 (2016).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett, “Deep learning techniques for inverse problems in imaging,” IEEE J. Sel. Areas Inform. Theor. 1, 39–56 (2020).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
C. You, M. A. Quiroz-Juárez, A. Lambert, N. Bhusal, C. Dong, A. Perez-Leija, A. Javaid, R. de. J. León-Montiel, and O. S. Magaña-Loaiza, “Identification of light sources using machine learning,” Appl. Phys. Rev. 7, 021404 (2020).
[Crossref]
X. Li, J. Dong, B. Li, Y. Zhang, Y. Zhang, A. Veeraraghavan, and X. Ji, “Fast confocal microscopy imaging based on deep learning,” in IEEE International Conference on Computational Photography (ICCP) (2020), pp. 1–12.
M. Närhi, L. Salmela, J. Toivonen, C. Billet, J. M. Dudley, and G. Genty, “Machine learning analysis of extreme events in optical fibre modulation instability,” Nat. Commun. 9, 4923 (2018).
[Crossref]
A. A. Melnikov, H. P. Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, and H. J. Briegel, “Active learning machine learns to create new quantum experiments,” Proc. Natl. Acad. Sci. USA 115, 1221–1226 (2018).
[Crossref]
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
M. M. R. Elsawy, S. Lanteri, R. Duvigneau, J. A. Fan, and P. Genevet, “Numerical optimization methods for metasurfaces,” Laser Photonics Rev. 14, 1900445 (2020).
[Crossref]
M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).
[Crossref]
J. M. Ede and R. Beanland, “Partial scanning transmission electron microscopy with deep learning,” Sci. Rep. 10, 8332 (2020).
[Crossref]
N. M. Estakhri, B. Edwards, and N. Engheta, “Inverse-designed metastructures that solve equations,” Science 363, 1333–1338 (2019).
[Crossref]
P. Mühlschlegel, H.-J. Eisler, O. J. F. Martin, B. Hecht, and D. W. Pohl, “Resonant optical antennas,” Science 308, 1607–1609 (2005).
[Crossref]
B. Midtvedt, E. Olsén, F. Eklund, F. Höök, C. B. Adiels, G. Volpe, and D. Midtvedt, “Holographic characterisation of subwavelength particles enhanced by deep learning,” arXiv:2006.11154 (2020).
M. M. R. Elsawy, S. Lanteri, R. Duvigneau, J. A. Fan, and P. Genevet, “Numerical optimization methods for metasurfaces,” Laser Photonics Rev. 14, 1900445 (2020).
[Crossref]
M. Elzouka, C. Yang, A. Albert, S. Lubner, and R. S. Prasher, “Interpretable inverse design of particle spectral emissivity using machine learning,” arXiv:2002.04223 (2020).
N. M. Estakhri, B. Edwards, and N. Engheta, “Inverse-designed metastructures that solve equations,” Science 363, 1333–1338 (2019).
[Crossref]
M. Krenn, M. Erhard, and A. Zeilinger, “Computer-inspired quantum experiments,” Nat. Rev. Phys. 2, 649–661 (2020).
[Crossref]
N. M. Estakhri, B. Edwards, and N. Engheta, “Inverse-designed metastructures that solve equations,” Science 363, 1333–1338 (2019).
[Crossref]
M. M. R. Elsawy, S. Lanteri, R. Duvigneau, J. A. Fan, and P. Genevet, “Numerical optimization methods for metasurfaces,” Laser Photonics Rev. 14, 1900445 (2020).
[Crossref]
J. Jiang and J. A. Fan, “Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks,” Nanophotonics 10, 361–369 (2020).
[Crossref]
F. Wen, J. Jiang, and J. A. Fan, “Robust freeform metasurface design based on progressively growing generative networks,” ACS Photonics 7, 2098–2104 (2020).
[Crossref]
J. Jiang and J. A. Fan, “Global optimization of dielectric metasurfaces using a physics-driven neural network,” Nano Lett. 19, 5366–5372 (2019).
[Crossref]
J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13, 8872–8878 (2019).
[Crossref]
S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, and D. H. Werner, “Review of numerical optimization techniques for meta-device design [Invited],” Opt. Mater. Express 9, 1842–1863 (2019).
[Crossref]
J. Jiang, M. Chen, and J. A. Fan, “Deep neural networks for the evaluation and design of photonic devices,” arXiv:2007.00084 (2020).
S. Wang, K. Fan, N. Luo, Y. Cao, F. Wu, C. Zhang, K. A. Heller, and L. You, “Massive computational acceleration by using neural networks to emulate mechanism-based biological models,” Nat. Commun. 10, 4354 (2019).
[Crossref]
T. W. Hughes, I. A. D. Williamson, M. Minkov, and S. Fan, “Wave physics as an analog recurrent neural network,” Sci. Adv. 5, eaay6946 (2019).
[Crossref]
Z. Fang and J. Zhan, “Deep physical informed neural networks for metamaterial design,” IEEE Access 8, 24506–24513 (2020).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
T. Feichtner, O. Selig, M. Kiunke, and B. Hecht, “Evolutionary optimization of optical antennas,” Phys. Rev. Lett. 109, 127701 (2012).
[Crossref]
E. Nehme, D. Freedman, R. Gordon, B. Ferdman, L. E. Weiss, O. Alalouf, T. Naor, R. Orange, T. Michaeli, and Y. Shechtman, “DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning,” Nat. Methods 17, 734–740 (2020).
[Crossref]
A. Youssry, R. J. Chapman, A. Peruzzo, C. Ferrie, and M. Tomamichel, “Modeling and control of a reconfigurable photonic circuit using deep learning,” Quantum Sci. Technol. 5, 025001 (2020).
[Crossref]
M. Krenn, M. Malik, R. Fickler, R. Lapkiewicz, and A. Zeilinger, “Automated search for new quantum experiments,” Phys. Rev. Lett. 116, 090405 (2016).
[Crossref]
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
S. Popoff, G. Lerosey, M. Fink, A. C. Boccara, and S. Gigan, “Image transmission through an opaque material,” Nat. Commun. 1, 81 (2010).
[Crossref]
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” arXiv:1505.04597 (2015).
F. Zangeneh-Nejad, D. L. Sounas, A. Alù, and R. Fleury, “Analogue computing with metamaterials,” Nat. Rev. Mater. 6, 207–225 (2021).
[Crossref]
J. M. Newby, A. M. Schaefer, P. T. Lee, M. G. Forest, and S. K. Lai, “Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D,” Proc. Natl. Acad. Sci. USA 115, 9026–9031 (2018).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
S. An, B. Zheng, H. Tang, M. Y. Shalaginov, L. Zhou, H. Li, T. Gu, J. Hu, C. Fowler, and H. Zhang, “Multifunctional metasurface design with a generative adversarial network,” arXiv:1908.04851 (2020).
E. Nehme, D. Freedman, R. Gordon, B. Ferdman, L. E. Weiss, O. Alalouf, T. Naor, R. Orange, T. Michaeli, and Y. Shechtman, “DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning,” Nat. Methods 17, 734–740 (2020).
[Crossref]
U. Kürüm, P. R. Wiecha, R. French, and O. L. Muskens, “Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array,” Opt. Express 27, 20965–20979 (2019).
[Crossref]
R. French, S. Gigan, and O. L. Muskens, “Snapshot fiber spectral imaging using speckle correlations and compressive sensing,” Opt. Express 26, 32302–32316 (2018).
[Crossref]
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. 26, 4509–4522 (2017).
[Crossref]
S. L. Brunton, X. Fu, and J. N. Kutz, “Self-tuning fiber lasers,” IEEE J. Sel. Top. Quantum Electron. 20, 464–471 (2014).
[Crossref]
B. Gallinet, J. Butet, and O. J. F. Martin, “Numerical methods for nanophotonics: standard problems and future challenges,” Laser Photonics Rev. 9, 577–603 (2015).
[Crossref]
H. Gao, L. Sun, and J.-X. Wang, “PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain,” J. Comput. Phys. 428, 110079 (2020).
[Crossref]
L. Gao, X. Li, D. Liu, L. Wang, and Z. Yu, “A bidirectional deep neural network for accurate silicon color design,” Adv. Mater. 31, 1905467 (2019).
[Crossref]
M. M. R. Elsawy, S. Lanteri, R. Duvigneau, J. A. Fan, and P. Genevet, “Numerical optimization methods for metasurfaces,” Laser Photonics Rev. 14, 1900445 (2020).
[Crossref]
P. Genevet, F. Capasso, F. Aieta, M. Khorasaninejad, and R. Devlin, “Recent advances in planar optics: from plasmonic to dielectric metasurfaces,” Optica 4, 139–152 (2017).
[Crossref]
Y.-T. Luo, P.-Q. Li, D.-T. Li, Y.-G. Peng, Z.-G. Geng, S.-H. Xie, Y. Li, A. Alù, J. Zhu, and X.-F. Zhu, “Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures,” Research 2020, 8757403 (2020).
[Crossref]
M. Närhi, L. Salmela, J. Toivonen, C. Billet, J. M. Dudley, and G. Genty, “Machine learning analysis of extreme events in optical fibre modulation instability,” Nat. Commun. 9, 4923 (2018).
[Crossref]
R. Selle, G. Vogt, T. Brixner, G. Gerber, R. Metzler, and W. Kinzel, “Modeling of light-matter interactions with neural networks,” Phys. Rev. A 76, 023810 (2007).
[Crossref]
A. Ghosh, D. J. Roth, L. H. Nicholls, W. P. Wardley, A. V. Zayats, and V. A. Podolskiy, “Machine learning—based diffractive imaging with subwavelength resolution,” arXiv:2005.03595 (2020).
R. French, S. Gigan, and O. L. Muskens, “Snapshot fiber spectral imaging using speckle correlations and compressive sensing,” Opt. Express 26, 32302–32316 (2018).
[Crossref]
S. Popoff, G. Lerosey, M. Fink, A. C. Boccara, and S. Gigan, “Image transmission through an opaque material,” Nat. Commun. 1, 81 (2010).
[Crossref]
P. R. Wiecha, P. R. Wiecha, C. Majorel, C. Girard, A. Cuche, V. Paillard, O. L. Muskens, and A. Arbouet, “Design of plasmonic directional antennas via evolutionary optimization,” Opt. Express 27, 29069–29081 (2019).
[Crossref]
P. R. Wiecha, A. Arbouet, C. Girard, A. Lecestre, G. Larrieu, and V. Paillard, “Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas,” Nat. Nanotechnol. 12, 163–169 (2017).
[Crossref]
G. C. des Francs, J. Barthes, A. Bouhelier, J. C. Weeber, A. Dereux, A. Cuche, and C. Girard, “Plasmonic Purcell factor and coupling efficiency to surface plasmons. Implications for addressing and controlling optical nanosources,” J. Opt. 18, 094005 (2016).
[Crossref]
C. Girard, “Near fields in nanostructures,” Rep. Prog. Phys. 68, 1883–1933 (2005).
[Crossref]
A. K. González-Alcalde, R. Salas-Montiel, V. Kalt, S. Blaize, and D. Macías, “Engineering colors in all-dielectric metasurfaces: metamodeling approach,” Opt. Lett. 45, 89–92 (2020).
[Crossref]
V. Kalt, A. K. González-Alcalde, S. Es-Saidi, R. Salas-Montiel, S. Blaize, and D. Macías, “Metamodeling of high-contrast-index gratings for color reproduction,” J. Opt. Soc. Am. A 36, 79–88 (2019).
[Crossref]
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT, 2016).
E. Nehme, D. Freedman, R. Gordon, B. Ferdman, L. E. Weiss, O. Alalouf, T. Naor, R. Orange, T. Michaeli, and Y. Shechtman, “DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning,” Nat. Methods 17, 734–740 (2020).
[Crossref]
Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydn, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2018).
[Crossref]
Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4, 1437–1443 (2017).
[Crossref]
E. Ashalley, K. Acheampong, L. V. Besteiro, L. V. Besteiro, P. Yu, A. Neogi, A. O. Govorov, A. O. Govorov, and Z. M. Wang, “Multitask deep-learning-based design of chiral plasmonic metamaterials,” Photon. Res. 8, 1213–1225 (2020).
[Crossref]
E. Ashalley, K. Acheampong, L. V. Besteiro, L. V. Besteiro, P. Yu, A. Neogi, A. O. Govorov, A. O. Govorov, and Z. M. Wang, “Multitask deep-learning-based design of chiral plasmonic metamaterials,” Photon. Res. 8, 1213–1225 (2020).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
C. L. Cortes, S. Adhikari, X. Ma, and S. K. Gray, “Accelerating quantum optics experiments with statistical learning,” Appl. Phys. Lett. 116, 184003 (2020).
[Crossref]
D. Piccinotti, K. F. MacDonald, S. Gregory, I. Youngs, and N. I. Zheludev, “Artificial intelligence for photonics and photonic materials,” Rep. Prog. Phys. 84, 012401 (2020).
[Crossref]
M. D. Hannel, A. Abdulali, M. O’Brien, and D. G. Grier, “Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles,” Opt. Express 26, 15221–15231 (2018).
[Crossref]
A. Yevick, M. Hannel, and D. G. Grier, “Machine-learning approach to holographic particle characterization,” Opt. Express 22, 26884–26890 (2014).
[Crossref]
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
S. An, B. Zheng, H. Tang, M. Y. Shalaginov, L. Zhou, H. Li, T. Gu, J. Hu, C. Fowler, and H. Zhang, “Multifunctional metasurface design with a generative adversarial network,” arXiv:1908.04851 (2020).
Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydn, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2018).
[Crossref]
Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]
H. Wang, Z. Zheng, C. Ji, and L. J. Guo, “Automated multi-layer optical design via deep reinforcement learning,” Mach. Learn. Sci. Technol. 2, 025013 (2021).
[Crossref]
H. Wang, Z. Zheng, C. Ji, and L. J. Guo, “Automated multi-layer optical design via deep reinforcement learning,” Mach. Learn. Sci. Technol. (2020).
X. Porte, A. Skalli, N. Haghighi, S. Reitzenstein, J. A. Lott, and D. Brunner, “A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser,” arXiv:2012.11153 (2020).
B. Wang, J. C. Cancilla, J. S. Torrecilla, and H. Haick, “Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase,” Nano Lett. 14, 933–938 (2014).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36, 460–468 (2018).
[Crossref]
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
T. Feichtner, O. Selig, M. Kiunke, and B. Hecht, “Evolutionary optimization of optical antennas,” Phys. Rev. Lett. 109, 127701 (2012).
[Crossref]
P. Mühlschlegel, H.-J. Eisler, O. J. F. Martin, B. Hecht, and D. W. Pohl, “Resonant optical antennas,” Science 308, 1607–1609 (2005).
[Crossref]
R. S. Hegde, “Deep learning: a new tool for photonic nanostructure design,” Nanoscale Adv. 2, 1007–1023 (2020).
[Crossref]
R. S. Hegde, “Photonics inverse design: pairing deep neural networks with evolutionary algorithms,” IEEE J. Sel. Top. Quantum Electron. 26, 7700908 (2020).
[Crossref]
S. Wang, K. Fan, N. Luo, Y. Cao, F. Wu, C. Zhang, K. A. Heller, and L. You, “Massive computational acceleration by using neural networks to emulate mechanism-based biological models,” Nat. Commun. 10, 4354 (2019).
[Crossref]
Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, and A. Adibi, “Knowledge discovery in nanophotonics using geometric deep learning,” Adv. Intell. Syst. 2, 1900132 (2020).
[Crossref]
Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theor. Simul. 2, 1900088 (2019).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13, 8872–8878 (2019).
[Crossref]
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, B. Pham, J. Liang, D. Ho, M. W. Knight, and A. P. Raman, “Designing multiplexed supercell metasurfaces with tandem neural networks,” Nanophotonics 10, 1133–1143 (2021).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, Y. Kawagoe, D. Ho, M. Knight, and A. P. Raman, “Elucidating the behavior of nanophotonic structures through explainable machine learning algorithms,” ACS Photonics 7, 2309–2318 (2020).
[Crossref]
C. Yeung, J.-M. Tsai, Y. Kawagoe, B. King, D. Ho, and A. P. Raman, “Elucidating the design and behavior of nanophotonic structures through explainable convolutional neural networks,” arXiv:2003.06075 (2020).
S. Shao, S. Shao, K. Mallery, K. Mallery, S. S. Kumar, S. S. Kumar, J. Hong, and J. Hong, “Machine learning holography for 3D particle field imaging,” Opt. Express 28, 2987–2999 (2020).
[Crossref]
S. Shao, S. Shao, K. Mallery, K. Mallery, S. S. Kumar, S. S. Kumar, J. Hong, and J. Hong, “Machine learning holography for 3D particle field imaging,” Opt. Express 28, 2987–2999 (2020).
[Crossref]
B. Midtvedt, E. Olsén, F. Eklund, F. Höök, C. B. Adiels, G. Volpe, and D. Midtvedt, “Holographic characterisation of subwavelength particles enhanced by deep learning,” arXiv:2006.11154 (2020).
Y. Nishizaki, R. Horisaki, K. Kitaguchi, M. Saito, and J. Tanida, “Analysis of non-iterative phase retrieval based on machine learning,” Opt. Rev. 27, 136–141 (2020).
[Crossref]
R. Horisaki, R. Takagi, and J. Tanida, “Learning-based imaging through scattering media,” Opt. Express 24, 13738–13743 (2016).
[Crossref]
J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13, 8872–8878 (2019).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
S. An, B. Zheng, H. Tang, M. Y. Shalaginov, L. Zhou, H. Li, T. Gu, J. Hu, C. Fowler, and H. Zhang, “Multifunctional metasurface design with a generative adversarial network,” arXiv:1908.04851 (2020).
C. C. Nadell, B. Huang, J. M. Malof, and W. J. Padilla, “Deep learning for accelerated all-dielectric metasurface design,” Opt. Express 27, 27523–27535 (2019).
[Crossref]
J. Zhou, B. Huang, Z. Yan, and J.-C. G. Bünzli, “Emerging role of machine learning in light-matter interaction,” Light Sci. Appl. 8, 1 (2019).
[Crossref]
P. Zhang, S. Liu, A. Chaurasia, D. Ma, M. J. Mlodzianoski, E. Culurciello, and F. Huang, “Analyzing complex single-molecule emission patterns with deep learning,” Nat. Methods 15, 913–916 (2018).
[Crossref]
L. Huang, L. Xu, and A. E. Miroshnichenko, “Deep learning enabled nanophotonics,” in Advances in Deep Learning (InTech, 2020).
[Crossref]
F. Meng, X. Huang, and B. Jia, “Bi-directional evolutionary optimization for photonic band gap structures,” J. Comput. Phys. 302, 393–404 (2015).
[Crossref]
T. W. Hughes, I. A. D. Williamson, M. Minkov, and S. Fan, “Wave physics as an analog recurrent neural network,” Sci. Adv. 5, eaay6946 (2019).
[Crossref]
A. D. Phan, C. V. Nguyen, P. T. Linh, T. V. Huynh, V. D. Lam, A.-T. Le, and K. Wakabayashi, “Deep learning for the inverse design of mid-infrared graphene plasmons,” Crystals 10, 125 (2020).
[Crossref]
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, inception-ResNet and the impact of residual connections on learning,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (2016), pp. 4278–4284.
Z. A. Kudyshev, S. I. Bogdanov, T. Isacsson, A. V. Kildishev, A. Boltasseva, and V. M. Shalaev, “Rapid classification of quantum sources enabled by machine learning,” Adv. Quantum Technol. 3, 2000067 (2020).
[Crossref]
R. Iten, T. Metger, H. Wilming, L. del Rio, and R. Renner, “Discovering physical concepts with neural networks,” Phys. Rev. Lett. 124, 010508 (2020).
[Crossref]
J. Moughames, X. Porte, M. Thiel, G. Ulliac, L. Larger, M. Jacquot, M. Kadic, and D. Brunner, “Three-dimensional waveguide interconnects for scalable integration of photonic neural networks,” Optica 7, 640–646 (2020).
[Crossref]
G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett, “Deep learning techniques for inverse problems in imaging,” IEEE J. Sel. Areas Inform. Theor. 1, 39–56 (2020).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).
[Crossref]
C. You, M. A. Quiroz-Juárez, A. Lambert, N. Bhusal, C. Dong, A. Perez-Leija, A. Javaid, R. de. J. León-Montiel, and O. S. Magaña-Loaiza, “Identification of light sources using machine learning,” Appl. Phys. Rev. 7, 021404 (2020).
[Crossref]
J. S. Jensen and O. Sigmund, “Topology optimization for nano-photonics,” Laser Photonics Rev. 5, 308–321 (2011).
[Crossref]
M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).
[Crossref]
H. Wang, Z. Zheng, C. Ji, and L. J. Guo, “Automated multi-layer optical design via deep reinforcement learning,” Mach. Learn. Sci. Technol. 2, 025013 (2021).
[Crossref]
H. Wang, Z. Zheng, C. Ji, and L. J. Guo, “Automated multi-layer optical design via deep reinforcement learning,” Mach. Learn. Sci. Technol. (2020).
X. Li, J. Dong, B. Li, Y. Zhang, Y. Zhang, A. Veeraraghavan, and X. Ji, “Fast confocal microscopy imaging based on deep learning,” in IEEE International Conference on Computational Photography (ICCP) (2020), pp. 1–12.
F. Meng, X. Huang, and B. Jia, “Bi-directional evolutionary optimization for photonic band gap structures,” J. Comput. Phys. 302, 393–404 (2015).
[Crossref]
F. Wen, J. Jiang, and J. A. Fan, “Robust freeform metasurface design based on progressively growing generative networks,” ACS Photonics 7, 2098–2104 (2020).
[Crossref]
J. Jiang and J. A. Fan, “Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks,” Nanophotonics 10, 361–369 (2020).
[Crossref]
J. Jiang and J. A. Fan, “Global optimization of dielectric metasurfaces using a physics-driven neural network,” Nano Lett. 19, 5366–5372 (2019).
[Crossref]
J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13, 8872–8878 (2019).
[Crossref]
J. Jiang, M. Chen, and J. A. Fan, “Deep neural networks for the evaluation and design of photonic devices,” arXiv:2007.00084 (2020).
C. Zhang, J. Jin, W. Na, Q.-J. Zhang, and M. Yu, “Multivalued neural network inverse modeling and applications to microwave filters,” IEEE Trans. Microwave Theory Tech. 66, 3781–3797 (2018).
[Crossref]
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. 26, 4509–4522 (2017).
[Crossref]
S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W. Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12, 659–670 (2018).
[Crossref]
T. Chugh, C. Sun, H. Wang, and Y. Jin, “Surrogate-assisted evolutionary optimization of large problems,” in High-Performance Simulation-Based Optimization, T. Bartz-Beielstein, B. Filipič, P. Korošec, and E.-G. Talbi, eds. (Springer, 2020), pp. 165–187.
Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (2019).
[Crossref]
J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4, eaar4206 (2018).
[Crossref]
Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M.-H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]
J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4, eaar4206 (2018).
[Crossref]
R. Pestourie, Y. Mroueh, T. V. Nguyen, P. Das, and S. G. Johnson, “Active learning of deep surrogates for PDEs: application to metasurface design,” arXiv:2008.12649 (2020).
Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M.-H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]
Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M.-H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]
H. Kabir, Y. Wang, M. Yu, and Q.-J. Zhang, “Neural network inverse modeling and applications to microwave filter design,” IEEE Trans. Microwave Theory Tech. 56, 867–879 (2008).
[Crossref]
J. Moughames, X. Porte, M. Thiel, G. Ulliac, L. Larger, M. Jacquot, M. Kadic, and D. Brunner, “Three-dimensional waveguide interconnects for scalable integration of photonic neural networks,” Optica 7, 640–646 (2020).
[Crossref]
M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).
[Crossref]
A. K. González-Alcalde, R. Salas-Montiel, V. Kalt, S. Blaize, and D. Macías, “Engineering colors in all-dielectric metasurfaces: metamodeling approach,” Opt. Lett. 45, 89–92 (2020).
[Crossref]
V. Kalt, A. K. González-Alcalde, S. Es-Saidi, R. Salas-Montiel, S. Blaize, and D. Macías, “Metamodeling of high-contrast-index gratings for color reproduction,” J. Opt. Soc. Am. A 36, 79–88 (2019).
[Crossref]
Y. Li, Y. Wang, S. Qi, Q. Ren, L. Kang, S. D. Campbell, P. L. Werner, and D. H. Werner, “Predicting scattering from complex nano-structures via deep learning,” IEEE Access 8, 139983 (2020).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M.-H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
Y. Chen, L. Lu, G. E. Karniadakis, and L. D. Negro, “Physics-informed neural networks for inverse problems in nano-optics and metamaterials,” Opt Express 28, 11618–11633 (2020).
[Crossref]
M. Raissi, A. Yazdani, and G. E. Karniadakis, “Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations,” Science 367, 1026–1030 (2020).
[Crossref]
M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” J. Comput. Phys. 378, 686–707 (2019).
[Crossref]
M. Kauranen and A. V. Zayats, “Nonlinear plasmonics,” Nat. Photonics 6, 737–748 (2012).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, Y. Kawagoe, D. Ho, M. Knight, and A. P. Raman, “Elucidating the behavior of nanophotonic structures through explainable machine learning algorithms,” ACS Photonics 7, 2309–2318 (2020).
[Crossref]
C. Yeung, J.-M. Tsai, Y. Kawagoe, B. King, D. Ho, and A. P. Raman, “Elucidating the design and behavior of nanophotonic structures through explainable convolutional neural networks,” arXiv:2003.06075 (2020).
D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5, 1365–1369 (2018).
[Crossref]
Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, and A. Adibi, “Knowledge discovery in nanophotonics using geometric deep learning,” Adv. Intell. Syst. 2, 1900132 (2020).
[Crossref]
Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theor. Simul. 2, 1900088 (2019).
[Crossref]
Y. Kiarashinejad, S. Abdollahramezani, and A. Adibi, “Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures,” arXiv:1902.03865 (2019).
Z. A. Kudyshev, A. V. Kildishev, V. M. Shalaev, and A. Boltasseva, “Machine learning assisted global optimization of photonic devices,” Nanophotonics 10, 371–383 (2020).
[Crossref]
Z. A. Kudyshev, S. I. Bogdanov, T. Isacsson, A. V. Kildishev, A. Boltasseva, and V. M. Shalaev, “Rapid classification of quantum sources enabled by machine learning,” Adv. Quantum Technol. 3, 2000067 (2020).
[Crossref]
T. Badloe, I. Kim, and J. Rho, “Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning,” Phys. Chem. Chem. Phys. 22, 2337–2342 (2020).
[Crossref]
Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M.-H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, B. Pham, J. Liang, D. Ho, M. W. Knight, and A. P. Raman, “Designing multiplexed supercell metasurfaces with tandem neural networks,” Nanophotonics 10, 1133–1143 (2021).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, Y. Kawagoe, D. Ho, M. Knight, and A. P. Raman, “Elucidating the behavior of nanophotonic structures through explainable machine learning algorithms,” ACS Photonics 7, 2309–2318 (2020).
[Crossref]
C. Yeung, J.-M. Tsai, Y. Kawagoe, B. King, D. Ho, and A. P. Raman, “Elucidating the design and behavior of nanophotonic structures through explainable convolutional neural networks,” arXiv:2003.06075 (2020).
D. P. Kingma and M. Welling, “An introduction to variational autoencoders,” Found. Trends Mach. Learn. 12, 307–392 (2019).
[Crossref]
R. Selle, G. Vogt, T. Brixner, G. Gerber, R. Metzler, and W. Kinzel, “Modeling of light-matter interactions with neural networks,” Phys. Rev. A 76, 023810 (2007).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
Y. Nishizaki, R. Horisaki, K. Kitaguchi, M. Saito, and J. Tanida, “Analysis of non-iterative phase retrieval based on machine learning,” Opt. Rev. 27, 136–141 (2020).
[Crossref]
T. Feichtner, O. Selig, M. Kiunke, and B. Hecht, “Evolutionary optimization of optical antennas,” Phys. Rev. Lett. 109, 127701 (2012).
[Crossref]
A. I. Kuznetsov, A. E. Miroshnichenko, M. L. Brongersma, Y. S. Kivshar, and B. Luk’yanchuk, “Optically resonant dielectric nanostructures,” Science 354, aag2472 (2016).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
C. Yeung, J.-M. Tsai, B. King, Y. Kawagoe, D. Ho, M. Knight, and A. P. Raman, “Elucidating the behavior of nanophotonic structures through explainable machine learning algorithms,” ACS Photonics 7, 2309–2318 (2020).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, B. Pham, J. Liang, D. Ho, M. W. Knight, and A. P. Raman, “Designing multiplexed supercell metasurfaces with tandem neural networks,” Nanophotonics 10, 1133–1143 (2021).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light Sci. Appl. 7, 69 (2018).
[Crossref]
J. Su, D. V. Vargas, and S. Kouichi, “One pixel attack for fooling deep neural networks,” IEEE Trans. Evol. Comput. 23, 828–841 (2019).
[Crossref]
Y. Zhu, N. Zabaras, P.-S. Koutsourelakis, and P. Perdikaris, “Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data,” J. Comput. Phys. 394, 56–81 (2019).
[Crossref]
A. M. Palmieri, E. Kovlakov, F. Bianchi, D. Yudin, S. Straupe, J. D. Biamonte, and S. Kulik, “Experimental neural network enhanced quantum tomography,” npj Quantum Inf. 6, 20 (2020).
[Crossref]
M. Krenn, M. Erhard, and A. Zeilinger, “Computer-inspired quantum experiments,” Nat. Rev. Phys. 2, 649–661 (2020).
[Crossref]
A. A. Melnikov, H. P. Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, and H. J. Briegel, “Active learning machine learns to create new quantum experiments,” Proc. Natl. Acad. Sci. USA 115, 1221–1226 (2018).
[Crossref]
M. Krenn, M. Malik, R. Fickler, R. Lapkiewicz, and A. Zeilinger, “Automated search for new quantum experiments,” Phys. Rev. Lett. 116, 090405 (2016).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15, 77–90 (2020).
[Crossref]
Z. A. Kudyshev, A. V. Kildishev, V. M. Shalaev, and A. Boltasseva, “Machine learning assisted global optimization of photonic devices,” Nanophotonics 10, 371–383 (2020).
[Crossref]
Z. A. Kudyshev, S. I. Bogdanov, T. Isacsson, A. V. Kildishev, A. Boltasseva, and V. M. Shalaev, “Rapid classification of quantum sources enabled by machine learning,” Adv. Quantum Technol. 3, 2000067 (2020).
[Crossref]
A. M. Palmieri, E. Kovlakov, F. Bianchi, D. Yudin, S. Straupe, J. D. Biamonte, and S. Kulik, “Experimental neural network enhanced quantum tomography,” npj Quantum Inf. 6, 20 (2020).
[Crossref]
A. Mall, A. Patil, D. Tamboli, A. Sethi, and A. Kumar, “Fast design of plasmonic metasurfaces enabled by deep learning,” J. Phys. D 53, 49LT01 (2020).
[Crossref]
S. Shao, S. Shao, K. Mallery, K. Mallery, S. S. Kumar, S. S. Kumar, J. Hong, and J. Hong, “Machine learning holography for 3D particle field imaging,” Opt. Express 28, 2987–2999 (2020).
[Crossref]
S. Shao, S. Shao, K. Mallery, K. Mallery, S. S. Kumar, S. S. Kumar, J. Hong, and J. Hong, “Machine learning holography for 3D particle field imaging,” Opt. Express 28, 2987–2999 (2020).
[Crossref]
T. Baumeister, S. L. Brunton, and J. N. Kutz, “Deep learning and model predictive control for self-tuning mode-locked lasers,” J. Opt. Soc. Am. B 35, 617–626 (2018).
[Crossref]
J. N. Kutz and S. L. Brunton, “Intelligent systems for stabilizing mode-locked lasers and frequency combs: machine learning and equation-free control paradigms for self-tuning optics,” Nanophotonics 4, 459–471 (2015).
[Crossref]
S. L. Brunton, X. Fu, and J. N. Kutz, “Self-tuning fiber lasers,” IEEE J. Sel. Top. Quantum Electron. 20, 464–471 (2014).
[Crossref]
A. I. Kuznetsov, A. E. Miroshnichenko, M. L. Brongersma, Y. S. Kivshar, and B. Luk’yanchuk, “Optically resonant dielectric nanostructures,” Science 354, aag2472 (2016).
[Crossref]
J. M. Newby, A. M. Schaefer, P. T. Lee, M. G. Forest, and S. K. Lai, “Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D,” Proc. Natl. Acad. Sci. USA 115, 9026–9031 (2018).
[Crossref]
J. Wang, F. Sciarrino, A. Laing, and M. G. Thompson, “Integrated photonic quantum technologies,” Nat. Photonics 14, 273–284 (2020).
[Crossref]
A. D. Phan, C. V. Nguyen, P. T. Linh, T. V. Huynh, V. D. Lam, A.-T. Le, and K. Wakabayashi, “Deep learning for the inverse design of mid-infrared graphene plasmons,” Crystals 10, 125 (2020).
[Crossref]
C. You, M. A. Quiroz-Juárez, A. Lambert, N. Bhusal, C. Dong, A. Perez-Leija, A. Javaid, R. de. J. León-Montiel, and O. S. Magaña-Loaiza, “Identification of light sources using machine learning,” Appl. Phys. Rev. 7, 021404 (2020).
[Crossref]
M. M. R. Elsawy, S. Lanteri, R. Duvigneau, J. A. Fan, and P. Genevet, “Numerical optimization methods for metasurfaces,” Laser Photonics Rev. 14, 1900445 (2020).
[Crossref]
D. W. Pohl, W. Denk, and M. Lanz, “Optical stethoscopy: image recording with resolution λ/20,” Appl. Phys. Lett. 44, 651–653 (1984).
[Crossref]
M. Krenn, M. Malik, R. Fickler, R. Lapkiewicz, and A. Zeilinger, “Automated search for new quantum experiments,” Phys. Rev. Lett. 116, 090405 (2016).
[Crossref]
J. Moughames, X. Porte, M. Thiel, G. Ulliac, L. Larger, M. Jacquot, M. Kadic, and D. Brunner, “Three-dimensional waveguide interconnects for scalable integration of photonic neural networks,” Optica 7, 640–646 (2020).
[Crossref]
P. R. Wiecha, A. Lecestre, N. Mallet, and G. Larrieu, “Pushing the limits of optical information storage using deep learning,” Nat. Nanotechnol. 14, 237–244 (2019).
[Crossref]
P. R. Wiecha, A. Arbouet, C. Girard, A. Lecestre, G. Larrieu, and V. Paillard, “Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas,” Nat. Nanotechnol. 12, 163–169 (2017).
[Crossref]
A. D. Phan, C. V. Nguyen, P. T. Linh, T. V. Huynh, V. D. Lam, A.-T. Le, and K. Wakabayashi, “Deep learning for the inverse design of mid-infrared graphene plasmons,” Crystals 10, 125 (2020).
[Crossref]
P. R. Wiecha, A. Lecestre, N. Mallet, and G. Larrieu, “Pushing the limits of optical information storage using deep learning,” Nat. Nanotechnol. 14, 237–244 (2019).
[Crossref]
P. R. Wiecha, A. Arbouet, C. Girard, A. Lecestre, G. Larrieu, and V. Paillard, “Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas,” Nat. Nanotechnol. 12, 163–169 (2017).
[Crossref]
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).
[Crossref]
I. Sajedian, H. Lee, and J. Rho, “Double-deep Q-learning to increase the efficiency of metasurface holograms,” Sci. Rep. 9, 10899 (2019).
[Crossref]
Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18, 6570–6576 (2018).
[Crossref]
J. M. Newby, A. M. Schaefer, P. T. Lee, M. G. Forest, and S. K. Lai, “Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D,” Proc. Natl. Acad. Sci. USA 115, 9026–9031 (2018).
[Crossref]
Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M.-H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]
W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36, 460–468 (2018).
[Crossref]
I. C. D. Lenton, G. Volpe, A. B. Stilgoe, T. A. Nieminen, and H. Rubinsztein-Dunlop, “Machine learning reveals complex behaviours in optically trapped particles,” Mach. Learn. Sci. Technol. 1, 045009 (2020).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
C. You, M. A. Quiroz-Juárez, A. Lambert, N. Bhusal, C. Dong, A. Perez-Leija, A. Javaid, R. de. J. León-Montiel, and O. S. Magaña-Loaiza, “Identification of light sources using machine learning,” Appl. Phys. Rev. 7, 021404 (2020).
[Crossref]
S. Popoff, G. Lerosey, M. Fink, A. C. Boccara, and S. Gigan, “Image transmission through an opaque material,” Nat. Commun. 1, 81 (2010).
[Crossref]
X. Li, J. Dong, B. Li, Y. Zhang, Y. Zhang, A. Veeraraghavan, and X. Ji, “Fast confocal microscopy imaging based on deep learning,” in IEEE International Conference on Computational Photography (ICCP) (2020), pp. 1–12.
Y.-T. Luo, P.-Q. Li, D.-T. Li, Y.-G. Peng, Z.-G. Geng, S.-H. Xie, Y. Li, A. Alù, J. Zhu, and X.-F. Zhu, “Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures,” Research 2020, 8757403 (2020).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
S. An, B. Zheng, H. Tang, M. Y. Shalaginov, L. Zhou, H. Li, T. Gu, J. Hu, C. Fowler, and H. Zhang, “Multifunctional metasurface design with a generative adversarial network,” arXiv:1908.04851 (2020).
Y.-T. Luo, P.-Q. Li, D.-T. Li, Y.-G. Peng, Z.-G. Geng, S.-H. Xie, Y. Li, A. Alù, J. Zhu, and X.-F. Zhu, “Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures,” Research 2020, 8757403 (2020).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
L. Gao, X. Li, D. Liu, L. Wang, and Z. Yu, “A bidirectional deep neural network for accurate silicon color design,” Adv. Mater. 31, 1905467 (2019).
[Crossref]
M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).
[Crossref]
X. Li, J. Dong, B. Li, Y. Zhang, Y. Zhang, A. Veeraraghavan, and X. Ji, “Fast confocal microscopy imaging based on deep learning,” in IEEE International Conference on Computational Photography (ICCP) (2020), pp. 1–12.
Y.-T. Luo, P.-Q. Li, D.-T. Li, Y.-G. Peng, Z.-G. Geng, S.-H. Xie, Y. Li, A. Alù, J. Zhu, and X.-F. Zhu, “Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures,” Research 2020, 8757403 (2020).
[Crossref]
Y. Li, Y. Wang, S. Qi, Q. Ren, L. Kang, S. D. Campbell, P. L. Werner, and D. H. Werner, “Predicting scattering from complex nano-structures via deep learning,” IEEE Access 8, 139983 (2020).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, B. Pham, J. Liang, D. Ho, M. W. Knight, and A. P. Raman, “Designing multiplexed supercell metasurfaces with tandem neural networks,” Nanophotonics 10, 1133–1143 (2021).
[Crossref]
Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydn, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2018).
[Crossref]
X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W. Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12, 659–670 (2018).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
A. D. Phan, C. V. Nguyen, P. T. Linh, T. V. Huynh, V. D. Lam, A.-T. Le, and K. Wakabayashi, “Deep learning for the inverse design of mid-infrared graphene plasmons,” Crystals 10, 125 (2020).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
L. Gao, X. Li, D. Liu, L. Wang, and Z. Yu, “A bidirectional deep neural network for accurate silicon color design,” Adv. Mater. 31, 1905467 (2019).
[Crossref]
D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5, 1365–1369 (2018).
[Crossref]
P. Zhang, S. Liu, A. Chaurasia, D. Ma, M. J. Mlodzianoski, E. Culurciello, and F. Huang, “Analyzing complex single-molecule emission patterns with deep learning,” Nat. Methods 15, 913–916 (2018).
[Crossref]
W. Ma and Y. Liu, “A data-efficient self-supervised deep learning model for design and characterization of nanophotonic structures,” Sci. China Phys. Mech. Astron. 63, 284212 (2020).
[Crossref]
W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15, 77–90 (2020).
[Crossref]
W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, “Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy,” Adv. Mater. 31, 1901111 (2019).
[Crossref]
W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12, 6326–6334 (2018).
[Crossref]
Z. Liu, L. Raju, D. Zhu, and W. Cai, “A hybrid strategy for the discovery and design of photonic structures,” IEEE J. Emerging Sel. Top. Circuits Syst. 10, 126–135 (2020).
[Crossref]
Z. Liu, Z. Liu, Z. Zhu, and W. Cai, “Topological encoding method for data-driven photonics inverse design,” Opt. Express 28, 4825–4835 (2020).
[Crossref]
Z. Liu, Z. Liu, Z. Zhu, and W. Cai, “Topological encoding method for data-driven photonics inverse design,” Opt. Express 28, 4825–4835 (2020).
[Crossref]
W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15, 77–90 (2020).
[Crossref]
Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18, 6570–6576 (2018).
[Crossref]
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light Sci. Appl. 7, 69 (2018).
[Crossref]
X. Porte, A. Skalli, N. Haghighi, S. Reitzenstein, J. A. Lott, and D. Brunner, “A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser,” arXiv:2012.11153 (2020).
R. Trivedi, L. Su, J. Lu, M. F. Schubert, and J. Vuckovic, “Data-driven acceleration of photonic simulations,” Sci. Rep. 9, 19728 (2019).
[Crossref]
Y. Chen, L. Lu, G. E. Karniadakis, and L. D. Negro, “Physics-informed neural networks for inverse problems in nano-optics and metamaterials,” Opt Express 28, 11618–11633 (2020).
[Crossref]
M. Elzouka, C. Yang, A. Albert, S. Lubner, and R. S. Prasher, “Interpretable inverse design of particle spectral emissivity using machine learning,” arXiv:2002.04223 (2020).
A. I. Kuznetsov, A. E. Miroshnichenko, M. L. Brongersma, Y. S. Kivshar, and B. Luk’yanchuk, “Optically resonant dielectric nanostructures,” Science 354, aag2472 (2016).
[Crossref]
A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Z. Med. Phys. 29, 102–127 (2019).
[Crossref]
A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Z. Med. Phys. 29, 102–127 (2019).
[Crossref]
S. Wang, K. Fan, N. Luo, Y. Cao, F. Wu, C. Zhang, K. A. Heller, and L. You, “Massive computational acceleration by using neural networks to emulate mechanism-based biological models,” Nat. Commun. 10, 4354 (2019).
[Crossref]
X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).
[Crossref]
Y.-T. Luo, P.-Q. Li, D.-T. Li, Y.-G. Peng, Z.-G. Geng, S.-H. Xie, Y. Li, A. Alù, J. Zhu, and X.-F. Zhu, “Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures,” Research 2020, 8757403 (2020).
[Crossref]
P. Zhang, S. Liu, A. Chaurasia, D. Ma, M. J. Mlodzianoski, E. Culurciello, and F. Huang, “Analyzing complex single-molecule emission patterns with deep learning,” Nat. Methods 15, 913–916 (2018).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
W. Ma and Y. Liu, “A data-efficient self-supervised deep learning model for design and characterization of nanophotonic structures,” Sci. China Phys. Mech. Astron. 63, 284212 (2020).
[Crossref]
W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15, 77–90 (2020).
[Crossref]
W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, “Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy,” Adv. Mater. 31, 1901111 (2019).
[Crossref]
W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12, 6326–6334 (2018).
[Crossref]
C. L. Cortes, S. Adhikari, X. Ma, and S. K. Gray, “Accelerating quantum optics experiments with statistical learning,” Appl. Phys. Lett. 116, 184003 (2020).
[Crossref]
D. Piccinotti, K. F. MacDonald, S. Gregory, I. Youngs, and N. I. Zheludev, “Artificial intelligence for photonics and photonic materials,” Rep. Prog. Phys. 84, 012401 (2020).
[Crossref]
A. K. González-Alcalde, R. Salas-Montiel, V. Kalt, S. Blaize, and D. Macías, “Engineering colors in all-dielectric metasurfaces: metamodeling approach,” Opt. Lett. 45, 89–92 (2020).
[Crossref]
V. Kalt, A. K. González-Alcalde, S. Es-Saidi, R. Salas-Montiel, S. Blaize, and D. Macías, “Metamodeling of high-contrast-index gratings for color reproduction,” J. Opt. Soc. Am. A 36, 79–88 (2019).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
C. You, M. A. Quiroz-Juárez, A. Lambert, N. Bhusal, C. Dong, A. Perez-Leija, A. Javaid, R. de. J. León-Montiel, and O. S. Magaña-Loaiza, “Identification of light sources using machine learning,” Appl. Phys. Rev. 7, 021404 (2020).
[Crossref]
P. R. Wiecha, P. R. Wiecha, C. Majorel, C. Girard, A. Cuche, V. Paillard, O. L. Muskens, and A. Arbouet, “Design of plasmonic directional antennas via evolutionary optimization,” Opt. Express 27, 29069–29081 (2019).
[Crossref]
M. V. Zhelyeznyakov, S. L. Brunton, and A. Majumdar, “Deep learning to accelerate Maxwell’s equations for inverse design of dielectric metasurfaces,” arXiv:2008.10632 (2020).
M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).
[Crossref]
M. Krenn, M. Malik, R. Fickler, R. Lapkiewicz, and A. Zeilinger, “Automated search for new quantum experiments,” Phys. Rev. Lett. 116, 090405 (2016).
[Crossref]
I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via deep learning,” Light Sci. Appl. 7, 60 (2018).
[Crossref]
A. Mall, A. Patil, D. Tamboli, A. Sethi, and A. Kumar, “Fast design of plasmonic metasurfaces enabled by deep learning,” J. Phys. D 53, 49LT01 (2020).
[Crossref]
S. Shao, S. Shao, K. Mallery, K. Mallery, S. S. Kumar, S. S. Kumar, J. Hong, and J. Hong, “Machine learning holography for 3D particle field imaging,” Opt. Express 28, 2987–2999 (2020).
[Crossref]
S. Shao, S. Shao, K. Mallery, K. Mallery, S. S. Kumar, S. S. Kumar, J. Hong, and J. Hong, “Machine learning holography for 3D particle field imaging,” Opt. Express 28, 2987–2999 (2020).
[Crossref]
P. R. Wiecha, A. Lecestre, N. Mallet, and G. Larrieu, “Pushing the limits of optical information storage using deep learning,” Nat. Nanotechnol. 14, 237–244 (2019).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
B. Moseley, A. Markham, and T. Nissen-Meyer, “Solving the wave equation with physics-informed deep learning,” arXiv:2006.11894 (2020).
A.-P. Blanchard-Dionne and O. J. F. Martin, “Successive training of a generative adversarial network for the design of an optical cloak,” OSA Contin. 4, 87–95 (2021).
[Crossref]
A.-P. Blanchard-Dionne and O. J. F. Martin, “Teaching optics to a machine learning network,” Opt. Lett. 45, 2922–2925 (2020).
[Crossref]
B. Gallinet, J. Butet, and O. J. F. Martin, “Numerical methods for nanophotonics: standard problems and future challenges,” Laser Photonics Rev. 9, 577–603 (2015).
[Crossref]
P. Mühlschlegel, H.-J. Eisler, O. J. F. Martin, B. Hecht, and D. W. Pohl, “Resonant optical antennas,” Science 308, 1607–1609 (2005).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. 26, 4509–4522 (2017).
[Crossref]
A. A. Melnikov, H. P. Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, and H. J. Briegel, “Active learning machine learns to create new quantum experiments,” Proc. Natl. Acad. Sci. USA 115, 1221–1226 (2018).
[Crossref]
F. Meng, X. Huang, and B. Jia, “Bi-directional evolutionary optimization for photonic band gap structures,” J. Comput. Phys. 302, 393–404 (2015).
[Crossref]
D. Mengu, Y. Rivenson, and A. Ozcan, “Scale-, shift- and rotation-invariant diffractive optical networks,” ACS Photon. 8, 324–334 (2021).
[Crossref]
R. Iten, T. Metger, H. Wilming, L. del Rio, and R. Renner, “Discovering physical concepts with neural networks,” Phys. Rev. Lett. 124, 010508 (2020).
[Crossref]
G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett, “Deep learning techniques for inverse problems in imaging,” IEEE J. Sel. Areas Inform. Theor. 1, 39–56 (2020).
[Crossref]
R. Selle, G. Vogt, T. Brixner, G. Gerber, R. Metzler, and W. Kinzel, “Modeling of light-matter interactions with neural networks,” Phys. Rev. A 76, 023810 (2007).
[Crossref]
E. Nehme, D. Freedman, R. Gordon, B. Ferdman, L. E. Weiss, O. Alalouf, T. Naor, R. Orange, T. Michaeli, and Y. Shechtman, “DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning,” Nat. Methods 17, 734–740 (2020).
[Crossref]
E. Nehme, L. E. Weiss, T. Michaeli, and Y. Shechtman, “Deep-STORM: super-resolution single-molecule microscopy by deep learning,” Optica 5, 458–464 (2018).
[Crossref]
B. Midtvedt, E. Olsén, F. Eklund, F. Höök, C. B. Adiels, G. Volpe, and D. Midtvedt, “Holographic characterisation of subwavelength particles enhanced by deep learning,” arXiv:2006.11154 (2020).
B. Midtvedt, E. Olsén, F. Eklund, F. Höök, C. B. Adiels, G. Volpe, and D. Midtvedt, “Holographic characterisation of subwavelength particles enhanced by deep learning,” arXiv:2006.11154 (2020).
T. W. Hughes, I. A. D. Williamson, M. Minkov, and S. Fan, “Wave physics as an analog recurrent neural network,” Sci. Adv. 5, eaay6946 (2019).
[Crossref]
A. I. Kuznetsov, A. E. Miroshnichenko, M. L. Brongersma, Y. S. Kivshar, and B. Luk’yanchuk, “Optically resonant dielectric nanostructures,” Science 354, aag2472 (2016).
[Crossref]
L. Huang, L. Xu, and A. E. Miroshnichenko, “Deep learning enabled nanophotonics,” in Advances in Deep Learning (InTech, 2020).
[Crossref]
P. Zhang, S. Liu, A. Chaurasia, D. Ma, M. J. Mlodzianoski, E. Culurciello, and F. Huang, “Analyzing complex single-molecule emission patterns with deep learning,” Nat. Methods 15, 913–916 (2018).
[Crossref]
S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W. Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12, 659–670 (2018).
[Crossref]
A. Sheverdin, F. Monticone, and C. Valagiannopoulos, “Photonic inverse design with neural networks: the case of invisibility in the visible,” Phys. Rev. Appl. 14, 024054 (2020).
[Crossref]
B. Moseley, A. Markham, and T. Nissen-Meyer, “Solving the wave equation with physics-informed deep learning,” arXiv:2006.11894 (2020).
N. Borhani, E. Kakkava, C. Moser, and D. Psaltis, “Learning to see through multimode fibers,” Optica 5, 960–966 (2018).
[Crossref]
B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light Sci. Appl. 7, 69 (2018).
[Crossref]
J. Moughames, X. Porte, M. Thiel, G. Ulliac, L. Larger, M. Jacquot, M. Kadic, and D. Brunner, “Three-dimensional waveguide interconnects for scalable integration of photonic neural networks,” Optica 7, 640–646 (2020).
[Crossref]
I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via deep learning,” Light Sci. Appl. 7, 60 (2018).
[Crossref]
R. Pestourie, Y. Mroueh, T. V. Nguyen, P. Das, and S. G. Johnson, “Active learning of deep surrogates for PDEs: application to metasurface design,” arXiv:2008.12649 (2020).
P. Mühlschlegel, H.-J. Eisler, O. J. F. Martin, B. Hecht, and D. W. Pohl, “Resonant optical antennas,” Science 308, 1607–1609 (2005).
[Crossref]
S. So, J. Mun, and J. Rho, “Simultaneous inverse design of materials and structures via deep learning: demonstration of dipole resonance engineering using core–shell nanoparticles,” ACS Appl. Mater. Interfaces 11, 24264–24268 (2019).
[Crossref]
P. R. Wiecha and O. L. Muskens, “Deep learning meets nanophotonics: a generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures,” Nano Lett. 20, 329–338 (2020).
[Crossref]
P. R. Wiecha, P. R. Wiecha, C. Majorel, C. Girard, A. Cuche, V. Paillard, O. L. Muskens, and A. Arbouet, “Design of plasmonic directional antennas via evolutionary optimization,” Opt. Express 27, 29069–29081 (2019).
[Crossref]
U. Kürüm, P. R. Wiecha, R. French, and O. L. Muskens, “Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array,” Opt. Express 27, 20965–20979 (2019).
[Crossref]
R. French, S. Gigan, and O. L. Muskens, “Snapshot fiber spectral imaging using speckle correlations and compressive sensing,” Opt. Express 26, 32302–32316 (2018).
[Crossref]
L.-J. Black, Y. Wang, C. H. de Groot, A. Arbouet, and O. L. Muskens, “Optimal polarization conversion in coupled dimer plasmonic nanoantennas for metasurfaces,” ACS Nano 8, 6390–6399 (2014).
[Crossref]
C. Zhang, J. Jin, W. Na, Q.-J. Zhang, and M. Yu, “Multivalued neural network inverse modeling and applications to microwave filters,” IEEE Trans. Microwave Theory Tech. 66, 3781–3797 (2018).
[Crossref]
S. D. Campbell, D. Z. Zhu, E. B. Whiting, J. Nagar, D. H. Werner, and P. L. Werner, “Advanced multi-objective and surrogate-assisted optimization of topologically diverse metasurface architectures,” Proc. SPIE 10719, 107190U (2018).
[Crossref]
I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via deep learning,” Light Sci. Appl. 7, 60 (2018).
[Crossref]
E. Nehme, D. Freedman, R. Gordon, B. Ferdman, L. E. Weiss, O. Alalouf, T. Naor, R. Orange, T. Michaeli, and Y. Shechtman, “DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning,” Nat. Methods 17, 734–740 (2020).
[Crossref]
M. Närhi, L. Salmela, J. Toivonen, C. Billet, J. M. Dudley, and G. Genty, “Machine learning analysis of extreme events in optical fibre modulation instability,” Nat. Commun. 9, 4923 (2018).
[Crossref]
A. A. Melnikov, H. P. Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, and H. J. Briegel, “Active learning machine learns to create new quantum experiments,” Proc. Natl. Acad. Sci. USA 115, 1221–1226 (2018).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
Y. Chen, L. Lu, G. E. Karniadakis, and L. D. Negro, “Physics-informed neural networks for inverse problems in nano-optics and metamaterials,” Opt Express 28, 11618–11633 (2020).
[Crossref]
E. Nehme, D. Freedman, R. Gordon, B. Ferdman, L. E. Weiss, O. Alalouf, T. Naor, R. Orange, T. Michaeli, and Y. Shechtman, “DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning,” Nat. Methods 17, 734–740 (2020).
[Crossref]
E. Nehme, L. E. Weiss, T. Michaeli, and Y. Shechtman, “Deep-STORM: super-resolution single-molecule microscopy by deep learning,” Optica 5, 458–464 (2018).
[Crossref]
E. Ashalley, K. Acheampong, L. V. Besteiro, L. V. Besteiro, P. Yu, A. Neogi, A. O. Govorov, A. O. Govorov, and Z. M. Wang, “Multitask deep-learning-based design of chiral plasmonic metamaterials,” Photon. Res. 8, 1213–1225 (2020).
[Crossref]
J. M. Newby, A. M. Schaefer, P. T. Lee, M. G. Forest, and S. K. Lai, “Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D,” Proc. Natl. Acad. Sci. USA 115, 9026–9031 (2018).
[Crossref]
J. Trisno, H. Wang, H. T. Wang, R. J. H. Ng, S. D. Rezaei, and J. K. W. Yang, “Applying machine learning to the optics of dielectric nano-blobs,” Adv. Photonics Res. 1, 2000068 (2020).
[Crossref]
A. D. Phan, C. V. Nguyen, P. T. Linh, T. V. Huynh, V. D. Lam, A.-T. Le, and K. Wakabayashi, “Deep learning for the inverse design of mid-infrared graphene plasmons,” Crystals 10, 125 (2020).
[Crossref]
R. Pestourie, Y. Mroueh, T. V. Nguyen, P. Das, and S. G. Johnson, “Active learning of deep surrogates for PDEs: application to metasurface design,” arXiv:2008.12649 (2020).
E. A. Ash and G. Nicholls, “Super-resolution aperture scanning microscope,” Nature 237, 510–512 (1972).
[Crossref]
A. Ghosh, D. J. Roth, L. H. Nicholls, W. P. Wardley, A. V. Zayats, and V. A. Podolskiy, “Machine learning—based diffractive imaging with subwavelength resolution,” arXiv:2005.03595 (2020).
I. C. D. Lenton, G. Volpe, A. B. Stilgoe, T. A. Nieminen, and H. Rubinsztein-Dunlop, “Machine learning reveals complex behaviours in optically trapped particles,” Mach. Learn. Sci. Technol. 1, 045009 (2020).
[Crossref]
Y. Nishizaki, R. Horisaki, K. Kitaguchi, M. Saito, and J. Tanida, “Analysis of non-iterative phase retrieval based on machine learning,” Opt. Rev. 27, 136–141 (2020).
[Crossref]
B. Moseley, A. Markham, and T. Nissen-Meyer, “Solving the wave equation with physics-informed deep learning,” arXiv:2006.11894 (2020).
T. Asano and S. Noda, “Iterative optimization of photonic crystal nanocavity designs by using deep neural networks,” Nanophotonics 8, 2243–2256 (2019).
[Crossref]
S. So, T. Badloe, J. Noh, J. Bravo-Abad, and J. Rho, “Deep learning enabled inverse design in nanophotonics,” Nanophotonics 9, 1041–1057 (2020).
[Crossref]
B. Midtvedt, E. Olsén, F. Eklund, F. Höök, C. B. Adiels, G. Volpe, and D. Midtvedt, “Holographic characterisation of subwavelength particles enhanced by deep learning,” arXiv:2006.11154 (2020).
G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett, “Deep learning techniques for inverse problems in imaging,” IEEE J. Sel. Areas Inform. Theor. 1, 39–56 (2020).
[Crossref]
E. Nehme, D. Freedman, R. Gordon, B. Ferdman, L. E. Weiss, O. Alalouf, T. Naor, R. Orange, T. Michaeli, and Y. Shechtman, “DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning,” Nat. Methods 17, 734–740 (2020).
[Crossref]
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
S. Osher and J. A. Sethian, “Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations,” J. Comput. Phys. 79, 12–49 (1988).
[Crossref]
T. Pu, J. Y. Ou, N. Papasimakis, and N. I. Zheludev, “Label-free deeply subwavelength optical microscopy,” Appl. Phys. Lett. 116, 131105 (2020).
[Crossref]
T. Pu, J.-Y. Ou, V. Savinov, G. Yuan, N. Papasimakis, and N. Zheludev, “Unlabeled far-field deeply subwavelength topological microscopy (DSTM),” Adv. Sci. 8, 2002886 (2020).
[Crossref]
W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36, 460–468 (2018).
[Crossref]
D. Mengu, Y. Rivenson, and A. Ozcan, “Scale-, shift- and rotation-invariant diffractive optical networks,” ACS Photon. 8, 324–334 (2021).
[Crossref]
G. Barbastathis, A. Ozcan, and G. Situ, “On the use of deep learning for computational imaging,” Optica 6, 921–943 (2019).
[Crossref]
Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydn, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2018).
[Crossref]
Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]
X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).
[Crossref]
Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4, 1437–1443 (2017).
[Crossref]
P. R. Wiecha, P. R. Wiecha, C. Majorel, C. Girard, A. Cuche, V. Paillard, O. L. Muskens, and A. Arbouet, “Design of plasmonic directional antennas via evolutionary optimization,” Opt. Express 27, 29069–29081 (2019).
[Crossref]
P. R. Wiecha, A. Arbouet, C. Girard, A. Lecestre, G. Larrieu, and V. Paillard, “Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas,” Nat. Nanotechnol. 12, 163–169 (2017).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
A. M. Palmieri, E. Kovlakov, F. Bianchi, D. Yudin, S. Straupe, J. D. Biamonte, and S. Kulik, “Experimental neural network enhanced quantum tomography,” npj Quantum Inf. 6, 20 (2020).
[Crossref]
T. Pu, J.-Y. Ou, V. Savinov, G. Yuan, N. Papasimakis, and N. Zheludev, “Unlabeled far-field deeply subwavelength topological microscopy (DSTM),” Adv. Sci. 8, 2002886 (2020).
[Crossref]
T. Pu, J. Y. Ou, N. Papasimakis, and N. I. Zheludev, “Label-free deeply subwavelength optical microscopy,” Appl. Phys. Lett. 116, 131105 (2020).
[Crossref]
Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M.-H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]
Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M.-H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]
A. Mall, A. Patil, D. Tamboli, A. Sethi, and A. Kumar, “Fast design of plasmonic metasurfaces enabled by deep learning,” J. Phys. D 53, 49LT01 (2020).
[Crossref]
J. B. Pendry, “Negative refraction makes a perfect lens,” Phys. Rev. Lett. 85, 3966–3969 (2000).
[Crossref]
Y.-T. Luo, P.-Q. Li, D.-T. Li, Y.-G. Peng, Z.-G. Geng, S.-H. Xie, Y. Li, A. Alù, J. Zhu, and X.-F. Zhu, “Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures,” Research 2020, 8757403 (2020).
[Crossref]
M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” J. Comput. Phys. 378, 686–707 (2019).
[Crossref]
Y. Zhu, N. Zabaras, P.-S. Koutsourelakis, and P. Perdikaris, “Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data,” J. Comput. Phys. 394, 56–81 (2019).
[Crossref]
C. You, M. A. Quiroz-Juárez, A. Lambert, N. Bhusal, C. Dong, A. Perez-Leija, A. Javaid, R. de. J. León-Montiel, and O. S. Magaña-Loaiza, “Identification of light sources using machine learning,” Appl. Phys. Rev. 7, 021404 (2020).
[Crossref]
A. Youssry, R. J. Chapman, A. Peruzzo, C. Ferrie, and M. Tomamichel, “Modeling and control of a reconfigurable photonic circuit using deep learning,” Quantum Sci. Technol. 5, 025001 (2020).
[Crossref]
R. Pestourie, Y. Mroueh, T. V. Nguyen, P. Das, and S. G. Johnson, “Active learning of deep surrogates for PDEs: application to metasurface design,” arXiv:2008.12649 (2020).
M. Schuld, I. Sinayskiy, and F. Petruccione, “An introduction to quantum machine learning,” Contemp. Phys. 56, 172–185 (2015).
[Crossref]
J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4, eaar4206 (2018).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, B. Pham, J. Liang, D. Ho, M. W. Knight, and A. P. Raman, “Designing multiplexed supercell metasurfaces with tandem neural networks,” Nanophotonics 10, 1133–1143 (2021).
[Crossref]
A. D. Phan, C. V. Nguyen, P. T. Linh, T. V. Huynh, V. D. Lam, A.-T. Le, and K. Wakabayashi, “Deep learning for the inverse design of mid-infrared graphene plasmons,” Crystals 10, 125 (2020).
[Crossref]
D. Piccinotti, K. F. MacDonald, S. Gregory, I. Youngs, and N. I. Zheludev, “Artificial intelligence for photonics and photonic materials,” Rep. Prog. Phys. 84, 012401 (2020).
[Crossref]
S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W. Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12, 659–670 (2018).
[Crossref]
A. Ghosh, D. J. Roth, L. H. Nicholls, W. P. Wardley, A. V. Zayats, and V. A. Podolskiy, “Machine learning—based diffractive imaging with subwavelength resolution,” arXiv:2005.03595 (2020).
P. Mühlschlegel, H.-J. Eisler, O. J. F. Martin, B. Hecht, and D. W. Pohl, “Resonant optical antennas,” Science 308, 1607–1609 (2005).
[Crossref]
D. W. Pohl, W. Denk, and M. Lanz, “Optical stethoscopy: image recording with resolution λ/20,” Appl. Phys. Lett. 44, 651–653 (1984).
[Crossref]
S. Popoff, G. Lerosey, M. Fink, A. C. Boccara, and S. Gigan, “Image transmission through an opaque material,” Nat. Commun. 1, 81 (2010).
[Crossref]
J. Moughames, X. Porte, M. Thiel, G. Ulliac, L. Larger, M. Jacquot, M. Kadic, and D. Brunner, “Three-dimensional waveguide interconnects for scalable integration of photonic neural networks,” Optica 7, 640–646 (2020).
[Crossref]
X. Porte, A. Skalli, N. Haghighi, S. Reitzenstein, J. A. Lott, and D. Brunner, “A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser,” arXiv:2012.11153 (2020).
Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, and A. Adibi, “Knowledge discovery in nanophotonics using geometric deep learning,” Adv. Intell. Syst. 2, 1900132 (2020).
[Crossref]
M. Elzouka, C. Yang, A. Albert, S. Lubner, and R. S. Prasher, “Interpretable inverse design of particle spectral emissivity using machine learning,” arXiv:2002.04223 (2020).
B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light Sci. Appl. 7, 69 (2018).
[Crossref]
N. Borhani, E. Kakkava, C. Moser, and D. Psaltis, “Learning to see through multimode fibers,” Optica 5, 960–966 (2018).
[Crossref]
T. Pu, J. Y. Ou, N. Papasimakis, and N. I. Zheludev, “Label-free deeply subwavelength optical microscopy,” Appl. Phys. Lett. 116, 131105 (2020).
[Crossref]
T. Pu, J.-Y. Ou, V. Savinov, G. Yuan, N. Papasimakis, and N. Zheludev, “Unlabeled far-field deeply subwavelength topological microscopy (DSTM),” Adv. Sci. 8, 2002886 (2020).
[Crossref]
Y. Li, Y. Wang, S. Qi, Q. Ren, L. Kang, S. D. Campbell, P. L. Werner, and D. H. Werner, “Predicting scattering from complex nano-structures via deep learning,” IEEE Access 8, 139983 (2020).
[Crossref]
Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (2019).
[Crossref]
X. Shi, T. Qiu, J. Wang, X. Zhao, and S. Qu, “Metasurface inverse design using machine learning approaches,” J. Phys. D 53, 275105 (2020).
[Crossref]
X. Shi, T. Qiu, J. Wang, X. Zhao, and S. Qu, “Metasurface inverse design using machine learning approaches,” J. Phys. D 53, 275105 (2020).
[Crossref]
Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (2019).
[Crossref]
C. You, M. A. Quiroz-Juárez, A. Lambert, N. Bhusal, C. Dong, A. Perez-Leija, A. Javaid, R. de. J. León-Montiel, and O. S. Magaña-Loaiza, “Identification of light sources using machine learning,” Appl. Phys. Rev. 7, 021404 (2020).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light Sci. Appl. 7, 69 (2018).
[Crossref]
M. Raissi, A. Yazdani, and G. E. Karniadakis, “Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations,” Science 367, 1026–1030 (2020).
[Crossref]
M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” J. Comput. Phys. 378, 686–707 (2019).
[Crossref]
Z. Liu, L. Raju, D. Zhu, and W. Cai, “A hybrid strategy for the discovery and design of photonic structures,” IEEE J. Emerging Sel. Top. Circuits Syst. 10, 126–135 (2020).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, B. Pham, J. Liang, D. Ho, M. W. Knight, and A. P. Raman, “Designing multiplexed supercell metasurfaces with tandem neural networks,” Nanophotonics 10, 1133–1143 (2021).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, Y. Kawagoe, D. Ho, M. Knight, and A. P. Raman, “Elucidating the behavior of nanophotonic structures through explainable machine learning algorithms,” ACS Photonics 7, 2309–2318 (2020).
[Crossref]
C. Yeung, J.-M. Tsai, Y. Kawagoe, B. King, D. Ho, and A. P. Raman, “Elucidating the design and behavior of nanophotonic structures through explainable convolutional neural networks,” arXiv:2003.06075 (2020).
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
X. Porte, A. Skalli, N. Haghighi, S. Reitzenstein, J. A. Lott, and D. Brunner, “A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser,” arXiv:2012.11153 (2020).
Y. Li, Y. Wang, S. Qi, Q. Ren, L. Kang, S. D. Campbell, P. L. Werner, and D. H. Werner, “Predicting scattering from complex nano-structures via deep learning,” IEEE Access 8, 139983 (2020).
[Crossref]
Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydn, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2018).
[Crossref]
R. Iten, T. Metger, H. Wilming, L. del Rio, and R. Renner, “Discovering physical concepts with neural networks,” Phys. Rev. Lett. 124, 010508 (2020).
[Crossref]
J. Trisno, H. Wang, H. T. Wang, R. J. H. Ng, S. D. Rezaei, and J. K. W. Yang, “Applying machine learning to the optics of dielectric nano-blobs,” Adv. Photonics Res. 1, 2000068 (2020).
[Crossref]
T. Badloe, I. Kim, and J. Rho, “Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning,” Phys. Chem. Chem. Phys. 22, 2337–2342 (2020).
[Crossref]
S. So, T. Badloe, J. Noh, J. Bravo-Abad, and J. Rho, “Deep learning enabled inverse design in nanophotonics,” Nanophotonics 9, 1041–1057 (2020).
[Crossref]
S. So, J. Mun, and J. Rho, “Simultaneous inverse design of materials and structures via deep learning: demonstration of dipole resonance engineering using core–shell nanoparticles,” ACS Appl. Mater. Interfaces 11, 24264–24268 (2019).
[Crossref]
S. So and J. Rho, “Designing nanophotonic structures using conditional deep convolutional generative adversarial networks,” Nanophotonics 8, 1255–1261 (2019).
[Crossref]
I. Sajedian, H. Lee, and J. Rho, “Double-deep Q-learning to increase the efficiency of metasurface holograms,” Sci. Rep. 9, 10899 (2019).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
D. Mengu, Y. Rivenson, and A. Ozcan, “Scale-, shift- and rotation-invariant diffractive optical networks,” ACS Photon. 8, 324–334 (2021).
[Crossref]
X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).
[Crossref]
Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]
Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydn, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2018).
[Crossref]
Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4, 1437–1443 (2017).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
Y. Augenstein and C. Rockstuhl, “Inverse design of nanophotonic devices with structural integrity,” ACS Photonics 7, 2190–2196 (2020).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18, 6570–6576 (2018).
[Crossref]
S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W. Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12, 659–670 (2018).
[Crossref]
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” arXiv:1505.04597 (2015).
A. Ghosh, D. J. Roth, L. H. Nicholls, W. P. Wardley, A. V. Zayats, and V. A. Podolskiy, “Machine learning—based diffractive imaging with subwavelength resolution,” arXiv:2005.03595 (2020).
I. C. D. Lenton, G. Volpe, A. B. Stilgoe, T. A. Nieminen, and H. Rubinsztein-Dunlop, “Machine learning reveals complex behaviours in optically trapped particles,” Mach. Learn. Sci. Technol. 1, 045009 (2020).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
G. M. Sacha and P. Varona, “Artificial intelligence in nanotechnology,” Nanotechnology 24, 452002 (2013).
[Crossref]
Y. Nishizaki, R. Horisaki, K. Kitaguchi, M. Saito, and J. Tanida, “Analysis of non-iterative phase retrieval based on machine learning,” Opt. Rev. 27, 136–141 (2020).
[Crossref]
I. Sajedian, H. Lee, and J. Rho, “Double-deep Q-learning to increase the efficiency of metasurface holograms,” Sci. Rep. 9, 10899 (2019).
[Crossref]
A. K. González-Alcalde, R. Salas-Montiel, V. Kalt, S. Blaize, and D. Macías, “Engineering colors in all-dielectric metasurfaces: metamodeling approach,” Opt. Lett. 45, 89–92 (2020).
[Crossref]
V. Kalt, A. K. González-Alcalde, S. Es-Saidi, R. Salas-Montiel, S. Blaize, and D. Macías, “Metamodeling of high-contrast-index gratings for color reproduction,” J. Opt. Soc. Am. A 36, 79–88 (2019).
[Crossref]
M. Närhi, L. Salmela, J. Toivonen, C. Billet, J. M. Dudley, and G. Genty, “Machine learning analysis of extreme events in optical fibre modulation instability,” Nat. Commun. 9, 4923 (2018).
[Crossref]
M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
T. Pu, J.-Y. Ou, V. Savinov, G. Yuan, N. Papasimakis, and N. Zheludev, “Unlabeled far-field deeply subwavelength topological microscopy (DSTM),” Adv. Sci. 8, 2002886 (2020).
[Crossref]
J. M. Newby, A. M. Schaefer, P. T. Lee, M. G. Forest, and S. K. Lai, “Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D,” Proc. Natl. Acad. Sci. USA 115, 9026–9031 (2018).
[Crossref]
R. Trivedi, L. Su, J. Lu, M. F. Schubert, and J. Vuckovic, “Data-driven acceleration of photonic simulations,” Sci. Rep. 9, 19728 (2019).
[Crossref]
M. Schuld, I. Sinayskiy, and F. Petruccione, “An introduction to quantum machine learning,” Contemp. Phys. 56, 172–185 (2015).
[Crossref]
J. Wang, F. Sciarrino, A. Laing, and M. G. Thompson, “Integrated photonic quantum technologies,” Nat. Photonics 14, 273–284 (2020).
[Crossref]
T. Feichtner, O. Selig, M. Kiunke, and B. Hecht, “Evolutionary optimization of optical antennas,” Phys. Rev. Lett. 109, 127701 (2012).
[Crossref]
S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, and D. H. Werner, “Review of numerical optimization techniques for meta-device design [Invited],” Opt. Mater. Express 9, 1842–1863 (2019).
[Crossref]
J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13, 8872–8878 (2019).
[Crossref]
R. Selle, T. Brixner, T. Bayer, M. Wollenhaupt, and T. Baumert, “Modelling of ultrafast coherent strong-field dynamics in potassium with neural networks,” J. Phys. B 41, 074019 (2008).
[Crossref]
R. Selle, G. Vogt, T. Brixner, G. Gerber, R. Metzler, and W. Kinzel, “Modeling of light-matter interactions with neural networks,” Phys. Rev. A 76, 023810 (2007).
[Crossref]
A. Mall, A. Patil, D. Tamboli, A. Sethi, and A. Kumar, “Fast design of plasmonic metasurfaces enabled by deep learning,” J. Phys. D 53, 49LT01 (2020).
[Crossref]
S. Osher and J. A. Sethian, “Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations,” J. Comput. Phys. 79, 12–49 (1988).
[Crossref]
Z. A. Kudyshev, A. V. Kildishev, V. M. Shalaev, and A. Boltasseva, “Machine learning assisted global optimization of photonic devices,” Nanophotonics 10, 371–383 (2020).
[Crossref]
Z. A. Kudyshev, S. I. Bogdanov, T. Isacsson, A. V. Kildishev, A. Boltasseva, and V. M. Shalaev, “Rapid classification of quantum sources enabled by machine learning,” Adv. Quantum Technol. 3, 2000067 (2020).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
S. An, B. Zheng, H. Tang, M. Y. Shalaginov, L. Zhou, H. Li, T. Gu, J. Hu, C. Fowler, and H. Zhang, “Multifunctional metasurface design with a generative adversarial network,” arXiv:1908.04851 (2020).
S. Shao, S. Shao, K. Mallery, K. Mallery, S. S. Kumar, S. S. Kumar, J. Hong, and J. Hong, “Machine learning holography for 3D particle field imaging,” Opt. Express 28, 2987–2999 (2020).
[Crossref]
S. Shao, S. Shao, K. Mallery, K. Mallery, S. S. Kumar, S. S. Kumar, J. Hong, and J. Hong, “Machine learning holography for 3D particle field imaging,” Opt. Express 28, 2987–2999 (2020).
[Crossref]
E. Nehme, D. Freedman, R. Gordon, B. Ferdman, L. E. Weiss, O. Alalouf, T. Naor, R. Orange, T. Michaeli, and Y. Shechtman, “DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning,” Nat. Methods 17, 734–740 (2020).
[Crossref]
E. Nehme, L. E. Weiss, T. Michaeli, and Y. Shechtman, “Deep-STORM: super-resolution single-molecule microscopy by deep learning,” Optica 5, 458–464 (2018).
[Crossref]
Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (2019).
[Crossref]
J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4, eaar4206 (2018).
[Crossref]
A. Sheverdin, F. Monticone, and C. Valagiannopoulos, “Photonic inverse design with neural networks: the case of invisibility in the visible,” Phys. Rev. Appl. 14, 024054 (2020).
[Crossref]
X. Shi, T. Qiu, J. Wang, X. Zhao, and S. Qu, “Metasurface inverse design using machine learning approaches,” J. Phys. D 53, 275105 (2020).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
S. Chan and E. L. Siegel, “Will machine learning end the viability of radiology as a thriving medical specialty?” Br. J. Radiol. 92, 20180416 (2018).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
J. S. Jensen and O. Sigmund, “Topology optimization for nano-photonics,” Laser Photonics Rev. 5, 308–321 (2011).
[Crossref]
M. Schuld, I. Sinayskiy, and F. Petruccione, “An introduction to quantum machine learning,” Contemp. Phys. 56, 172–185 (2015).
[Crossref]
X. Porte, A. Skalli, N. Haghighi, S. Reitzenstein, J. A. Lott, and D. Brunner, “A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser,” arXiv:2012.11153 (2020).
S. So, T. Badloe, J. Noh, J. Bravo-Abad, and J. Rho, “Deep learning enabled inverse design in nanophotonics,” Nanophotonics 9, 1041–1057 (2020).
[Crossref]
S. So, J. Mun, and J. Rho, “Simultaneous inverse design of materials and structures via deep learning: demonstration of dipole resonance engineering using core–shell nanoparticles,” ACS Appl. Mater. Interfaces 11, 24264–24268 (2019).
[Crossref]
S. So and J. Rho, “Designing nanophotonic structures using conditional deep convolutional generative adversarial networks,” Nanophotonics 8, 1255–1261 (2019).
[Crossref]
Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (2019).
[Crossref]
J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4, eaar4206 (2018).
[Crossref]
F. Zangeneh-Nejad, D. L. Sounas, A. Alù, and R. Fleury, “Analogue computing with metamaterials,” Nat. Rev. Mater. 6, 207–225 (2021).
[Crossref]
I. C. D. Lenton, G. Volpe, A. B. Stilgoe, T. A. Nieminen, and H. Rubinsztein-Dunlop, “Machine learning reveals complex behaviours in optically trapped particles,” Mach. Learn. Sci. Technol. 1, 045009 (2020).
[Crossref]
A. M. Palmieri, E. Kovlakov, F. Bianchi, D. Yudin, S. Straupe, J. D. Biamonte, and S. Kulik, “Experimental neural network enhanced quantum tomography,” npj Quantum Inf. 6, 20 (2020).
[Crossref]
J. Su, D. V. Vargas, and S. Kouichi, “One pixel attack for fooling deep neural networks,” IEEE Trans. Evol. Comput. 23, 828–841 (2019).
[Crossref]
R. Trivedi, L. Su, J. Lu, M. F. Schubert, and J. Vuckovic, “Data-driven acceleration of photonic simulations,” Sci. Rep. 9, 19728 (2019).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via deep learning,” Light Sci. Appl. 7, 60 (2018).
[Crossref]
T. Chugh, C. Sun, H. Wang, and Y. Jin, “Surrogate-assisted evolutionary optimization of large problems,” in High-Performance Simulation-Based Optimization, T. Bartz-Beielstein, B. Filipič, P. Korošec, and E.-G. Talbi, eds. (Springer, 2020), pp. 165–187.
H. Gao, L. Sun, and J.-X. Wang, “PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain,” J. Comput. Phys. 428, 110079 (2020).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, inception-ResNet and the impact of residual connections on learning,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (2016), pp. 4278–4284.
A. Mall, A. Patil, D. Tamboli, A. Sethi, and A. Kumar, “Fast design of plasmonic metasurfaces enabled by deep learning,” J. Phys. D 53, 49LT01 (2020).
[Crossref]
D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5, 1365–1369 (2018).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
S. An, B. Zheng, H. Tang, M. Y. Shalaginov, L. Zhou, H. Li, T. Gu, J. Hu, C. Fowler, and H. Zhang, “Multifunctional metasurface design with a generative adversarial network,” arXiv:1908.04851 (2020).
Y. Nishizaki, R. Horisaki, K. Kitaguchi, M. Saito, and J. Tanida, “Analysis of non-iterative phase retrieval based on machine learning,” Opt. Rev. 27, 136–141 (2020).
[Crossref]
R. Horisaki, R. Takagi, and J. Tanida, “Learning-based imaging through scattering media,” Opt. Express 24, 13738–13743 (2016).
[Crossref]
J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4, eaar4206 (2018).
[Crossref]
Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]
A. Argun, T. Thalheim, S. Bo, F. Cichos, and G. Volpe, “Enhanced force-field calibration via machine learning,” Appl. Phys. Rev. 7, 041404 (2020).
[Crossref]
J. Moughames, X. Porte, M. Thiel, G. Ulliac, L. Larger, M. Jacquot, M. Kadic, and D. Brunner, “Three-dimensional waveguide interconnects for scalable integration of photonic neural networks,” Optica 7, 640–646 (2020).
[Crossref]
J. Wang, F. Sciarrino, A. Laing, and M. G. Thompson, “Integrated photonic quantum technologies,” Nat. Photonics 14, 273–284 (2020).
[Crossref]
A. A. Melnikov, H. P. Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, and H. J. Briegel, “Active learning machine learns to create new quantum experiments,” Proc. Natl. Acad. Sci. USA 115, 1221–1226 (2018).
[Crossref]
M. Närhi, L. Salmela, J. Toivonen, C. Billet, J. M. Dudley, and G. Genty, “Machine learning analysis of extreme events in optical fibre modulation instability,” Nat. Commun. 9, 4923 (2018).
[Crossref]
A. Youssry, R. J. Chapman, A. Peruzzo, C. Ferrie, and M. Tomamichel, “Modeling and control of a reconfigurable photonic circuit using deep learning,” Quantum Sci. Technol. 5, 025001 (2020).
[Crossref]
B. Wang, J. C. Cancilla, J. S. Torrecilla, and H. Haick, “Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase,” Nano Lett. 14, 933–938 (2014).
[Crossref]
J. Trisno, H. Wang, H. T. Wang, R. J. H. Ng, S. D. Rezaei, and J. K. W. Yang, “Applying machine learning to the optics of dielectric nano-blobs,” Adv. Photonics Res. 1, 2000068 (2020).
[Crossref]
R. Trivedi, L. Su, J. Lu, M. F. Schubert, and J. Vuckovic, “Data-driven acceleration of photonic simulations,” Sci. Rep. 9, 19728 (2019).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, B. Pham, J. Liang, D. Ho, M. W. Knight, and A. P. Raman, “Designing multiplexed supercell metasurfaces with tandem neural networks,” Nanophotonics 10, 1133–1143 (2021).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, Y. Kawagoe, D. Ho, M. Knight, and A. P. Raman, “Elucidating the behavior of nanophotonic structures through explainable machine learning algorithms,” ACS Photonics 7, 2309–2318 (2020).
[Crossref]
C. Yeung, J.-M. Tsai, Y. Kawagoe, B. King, D. Ho, and A. P. Raman, “Elucidating the design and behavior of nanophotonic structures through explainable convolutional neural networks,” arXiv:2003.06075 (2020).
Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydn, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2018).
[Crossref]
J. Moughames, X. Porte, M. Thiel, G. Ulliac, L. Larger, M. Jacquot, M. Kadic, and D. Brunner, “Three-dimensional waveguide interconnects for scalable integration of photonic neural networks,” Optica 7, 640–646 (2020).
[Crossref]
R. Unni, K. Yao, and Y. Zheng, “Deep convolutional mixture density network for inverse design of layered photonic structures,” ACS Photonics 7, 2703–2712 (2020).
[Crossref]
K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” Nanophotonics 8, 339–366 (2019).
[Crossref]
M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).
[Crossref]
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. 26, 4509–4522 (2017).
[Crossref]
A. Sheverdin, F. Monticone, and C. Valagiannopoulos, “Photonic inverse design with neural networks: the case of invisibility in the visible,” Phys. Rev. Appl. 14, 024054 (2020).
[Crossref]
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, inception-ResNet and the impact of residual connections on learning,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (2016), pp. 4278–4284.
J. Su, D. V. Vargas, and S. Kouichi, “One pixel attack for fooling deep neural networks,” IEEE Trans. Evol. Comput. 23, 828–841 (2019).
[Crossref]
G. M. Sacha and P. Varona, “Artificial intelligence in nanotechnology,” Nanotechnology 24, 452002 (2013).
[Crossref]
M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).
[Crossref]
X. Li, J. Dong, B. Li, Y. Zhang, Y. Zhang, A. Veeraraghavan, and X. Ji, “Fast confocal microscopy imaging based on deep learning,” in IEEE International Conference on Computational Photography (ICCP) (2020), pp. 1–12.
X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).
[Crossref]
R. Selle, G. Vogt, T. Brixner, G. Gerber, R. Metzler, and W. Kinzel, “Modeling of light-matter interactions with neural networks,” Phys. Rev. A 76, 023810 (2007).
[Crossref]
I. C. D. Lenton, G. Volpe, A. B. Stilgoe, T. A. Nieminen, and H. Rubinsztein-Dunlop, “Machine learning reveals complex behaviours in optically trapped particles,” Mach. Learn. Sci. Technol. 1, 045009 (2020).
[Crossref]
A. Argun, T. Thalheim, S. Bo, F. Cichos, and G. Volpe, “Enhanced force-field calibration via machine learning,” Appl. Phys. Rev. 7, 041404 (2020).
[Crossref]
S. Helgadottir, A. Argun, and G. Volpe, “Digital video microscopy enhanced by deep learning,” Optica 6, 506–513 (2019).
[Crossref]
B. Midtvedt, E. Olsén, F. Eklund, F. Höök, C. B. Adiels, G. Volpe, and D. Midtvedt, “Holographic characterisation of subwavelength particles enhanced by deep learning,” arXiv:2006.11154 (2020).
R. Trivedi, L. Su, J. Lu, M. F. Schubert, and J. Vuckovic, “Data-driven acceleration of photonic simulations,” Sci. Rep. 9, 19728 (2019).
[Crossref]
S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W. Rodriguez, “Inverse design in nanophotonics,” Nat. Photonics 12, 659–670 (2018).
[Crossref]
A. D. Phan, C. V. Nguyen, P. T. Linh, T. V. Huynh, V. D. Lam, A.-T. Le, and K. Wakabayashi, “Deep learning for the inverse design of mid-infrared graphene plasmons,” Crystals 10, 125 (2020).
[Crossref]
B. Wang, J. C. Cancilla, J. S. Torrecilla, and H. Haick, “Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase,” Nano Lett. 14, 933–938 (2014).
[Crossref]
H. Wang, Z. Zheng, C. Ji, and L. J. Guo, “Automated multi-layer optical design via deep reinforcement learning,” Mach. Learn. Sci. Technol. 2, 025013 (2021).
[Crossref]
J. Trisno, H. Wang, H. T. Wang, R. J. H. Ng, S. D. Rezaei, and J. K. W. Yang, “Applying machine learning to the optics of dielectric nano-blobs,” Adv. Photonics Res. 1, 2000068 (2020).
[Crossref]
Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydn, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2018).
[Crossref]
Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4, 1437–1443 (2017).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
H. Wang, Z. Zheng, C. Ji, and L. J. Guo, “Automated multi-layer optical design via deep reinforcement learning,” Mach. Learn. Sci. Technol. (2020).
T. Chugh, C. Sun, H. Wang, and Y. Jin, “Surrogate-assisted evolutionary optimization of large problems,” in High-Performance Simulation-Based Optimization, T. Bartz-Beielstein, B. Filipič, P. Korošec, and E.-G. Talbi, eds. (Springer, 2020), pp. 165–187.
J. Trisno, H. Wang, H. T. Wang, R. J. H. Ng, S. D. Rezaei, and J. K. W. Yang, “Applying machine learning to the optics of dielectric nano-blobs,” Adv. Photonics Res. 1, 2000068 (2020).
[Crossref]
X. Shi, T. Qiu, J. Wang, X. Zhao, and S. Qu, “Metasurface inverse design using machine learning approaches,” J. Phys. D 53, 275105 (2020).
[Crossref]
J. Wang, F. Sciarrino, A. Laing, and M. G. Thompson, “Integrated photonic quantum technologies,” Nat. Photonics 14, 273–284 (2020).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
H. Gao, L. Sun, and J.-X. Wang, “PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain,” J. Comput. Phys. 428, 110079 (2020).
[Crossref]
L. Gao, X. Li, D. Liu, L. Wang, and Z. Yu, “A bidirectional deep neural network for accurate silicon color design,” Adv. Mater. 31, 1905467 (2019).
[Crossref]
S. Wang, K. Fan, N. Luo, Y. Cao, F. Wu, C. Zhang, K. A. Heller, and L. You, “Massive computational acceleration by using neural networks to emulate mechanism-based biological models,” Nat. Commun. 10, 4354 (2019).
[Crossref]
Y. Li, Y. Wang, S. Qi, Q. Ren, L. Kang, S. D. Campbell, P. L. Werner, and D. H. Werner, “Predicting scattering from complex nano-structures via deep learning,” IEEE Access 8, 139983 (2020).
[Crossref]
L.-J. Black, Y. Wang, C. H. de Groot, A. Arbouet, and O. L. Muskens, “Optimal polarization conversion in coupled dimer plasmonic nanoantennas for metasurfaces,” ACS Nano 8, 6390–6399 (2014).
[Crossref]
H. Kabir, Y. Wang, M. Yu, and Q.-J. Zhang, “Neural network inverse modeling and applications to microwave filter design,” IEEE Trans. Microwave Theory Tech. 56, 867–879 (2008).
[Crossref]
E. Ashalley, K. Acheampong, L. V. Besteiro, L. V. Besteiro, P. Yu, A. Neogi, A. O. Govorov, A. O. Govorov, and Z. M. Wang, “Multitask deep-learning-based design of chiral plasmonic metamaterials,” Photon. Res. 8, 1213–1225 (2020).
[Crossref]
A. Ghosh, D. J. Roth, L. H. Nicholls, W. P. Wardley, A. V. Zayats, and V. A. Podolskiy, “Machine learning—based diffractive imaging with subwavelength resolution,” arXiv:2005.03595 (2020).
G. C. des Francs, J. Barthes, A. Bouhelier, J. C. Weeber, A. Dereux, A. Cuche, and C. Girard, “Plasmonic Purcell factor and coupling efficiency to surface plasmons. Implications for addressing and controlling optical nanosources,” J. Opt. 18, 094005 (2016).
[Crossref]
Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydn, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2018).
[Crossref]
E. Nehme, D. Freedman, R. Gordon, B. Ferdman, L. E. Weiss, O. Alalouf, T. Naor, R. Orange, T. Michaeli, and Y. Shechtman, “DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning,” Nat. Methods 17, 734–740 (2020).
[Crossref]
E. Nehme, L. E. Weiss, T. Michaeli, and Y. Shechtman, “Deep-STORM: super-resolution single-molecule microscopy by deep learning,” Optica 5, 458–464 (2018).
[Crossref]
D. P. Kingma and M. Welling, “An introduction to variational autoencoders,” Found. Trends Mach. Learn. 12, 307–392 (2019).
[Crossref]
F. Wen, J. Jiang, and J. A. Fan, “Robust freeform metasurface design based on progressively growing generative networks,” ACS Photonics 7, 2098–2104 (2020).
[Crossref]
W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, “Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy,” Adv. Mater. 31, 1901111 (2019).
[Crossref]
Y. Li, Y. Wang, S. Qi, Q. Ren, L. Kang, S. D. Campbell, P. L. Werner, and D. H. Werner, “Predicting scattering from complex nano-structures via deep learning,” IEEE Access 8, 139983 (2020).
[Crossref]
D. Z. Zhu, E. B. Whiting, S. D. Campbell, D. B. Burckel, and D. H. Werner, “Optimal high efficiency 3D plasmonic metasurface elements revealed by lazy ants,” ACS Photonics 6, 2741–2748 (2019).
[Crossref]
S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, and D. H. Werner, “Review of numerical optimization techniques for meta-device design [Invited],” Opt. Mater. Express 9, 1842–1863 (2019).
[Crossref]
S. D. Campbell, D. Z. Zhu, E. B. Whiting, J. Nagar, D. H. Werner, and P. L. Werner, “Advanced multi-objective and surrogate-assisted optimization of topologically diverse metasurface architectures,” Proc. SPIE 10719, 107190U (2018).
[Crossref]
Y. Li, Y. Wang, S. Qi, Q. Ren, L. Kang, S. D. Campbell, P. L. Werner, and D. H. Werner, “Predicting scattering from complex nano-structures via deep learning,” IEEE Access 8, 139983 (2020).
[Crossref]
S. D. Campbell, D. Z. Zhu, E. B. Whiting, J. Nagar, D. H. Werner, and P. L. Werner, “Advanced multi-objective and surrogate-assisted optimization of topologically diverse metasurface architectures,” Proc. SPIE 10719, 107190U (2018).
[Crossref]
D. Z. Zhu, E. B. Whiting, S. D. Campbell, D. B. Burckel, and D. H. Werner, “Optimal high efficiency 3D plasmonic metasurface elements revealed by lazy ants,” ACS Photonics 6, 2741–2748 (2019).
[Crossref]
S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, and D. H. Werner, “Review of numerical optimization techniques for meta-device design [Invited],” Opt. Mater. Express 9, 1842–1863 (2019).
[Crossref]
S. D. Campbell, D. Z. Zhu, E. B. Whiting, J. Nagar, D. H. Werner, and P. L. Werner, “Advanced multi-objective and surrogate-assisted optimization of topologically diverse metasurface architectures,” Proc. SPIE 10719, 107190U (2018).
[Crossref]
P. R. Wiecha and O. L. Muskens, “Deep learning meets nanophotonics: a generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures,” Nano Lett. 20, 329–338 (2020).
[Crossref]
P. R. Wiecha, P. R. Wiecha, C. Majorel, C. Girard, A. Cuche, V. Paillard, O. L. Muskens, and A. Arbouet, “Design of plasmonic directional antennas via evolutionary optimization,” Opt. Express 27, 29069–29081 (2019).
[Crossref]
P. R. Wiecha, P. R. Wiecha, C. Majorel, C. Girard, A. Cuche, V. Paillard, O. L. Muskens, and A. Arbouet, “Design of plasmonic directional antennas via evolutionary optimization,” Opt. Express 27, 29069–29081 (2019).
[Crossref]
U. Kürüm, P. R. Wiecha, R. French, and O. L. Muskens, “Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array,” Opt. Express 27, 20965–20979 (2019).
[Crossref]
P. R. Wiecha, A. Lecestre, N. Mallet, and G. Larrieu, “Pushing the limits of optical information storage using deep learning,” Nat. Nanotechnol. 14, 237–244 (2019).
[Crossref]
P. R. Wiecha, A. Arbouet, C. Girard, A. Lecestre, G. Larrieu, and V. Paillard, “Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas,” Nat. Nanotechnol. 12, 163–169 (2017).
[Crossref]
G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett, “Deep learning techniques for inverse problems in imaging,” IEEE J. Sel. Areas Inform. Theor. 1, 39–56 (2020).
[Crossref]
T. W. Hughes, I. A. D. Williamson, M. Minkov, and S. Fan, “Wave physics as an analog recurrent neural network,” Sci. Adv. 5, eaay6946 (2019).
[Crossref]
R. Iten, T. Metger, H. Wilming, L. del Rio, and R. Renner, “Discovering physical concepts with neural networks,” Phys. Rev. Lett. 124, 010508 (2020).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via deep learning,” Light Sci. Appl. 7, 60 (2018).
[Crossref]
R. Selle, T. Brixner, T. Bayer, M. Wollenhaupt, and T. Baumert, “Modelling of ultrafast coherent strong-field dynamics in potassium with neural networks,” J. Phys. B 41, 074019 (2008).
[Crossref]
S. Wang, K. Fan, N. Luo, Y. Cao, F. Wu, C. Zhang, K. A. Heller, and L. You, “Massive computational acceleration by using neural networks to emulate mechanism-based biological models,” Nat. Commun. 10, 4354 (2019).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, and M. Finazzi, “Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation,” Nat. Nanotechnol. 10, 412–417 (2015).
[Crossref]
M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).
[Crossref]
J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” in Advances in Neural Information Processing Systems, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, eds. (Curran Associates, 2012), Vol. 25, pp. 341–349.
Y.-T. Luo, P.-Q. Li, D.-T. Li, Y.-G. Peng, Z.-G. Geng, S.-H. Xie, Y. Li, A. Alù, J. Zhu, and X.-F. Zhu, “Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures,” Research 2020, 8757403 (2020).
[Crossref]
B. Hu, B. Wu, D. Tan, J. Xu, J. Xu, Y. Chen, and Y. Chen, “Robust inverse-design of scattering spectrum in core-shell structure using modified denoising autoencoder neural network,” Opt. Express 27, 36276–36285 (2019).
[Crossref]
B. Hu, B. Wu, D. Tan, J. Xu, J. Xu, Y. Chen, and Y. Chen, “Robust inverse-design of scattering spectrum in core-shell structure using modified denoising autoencoder neural network,” Opt. Express 27, 36276–36285 (2019).
[Crossref]
J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” in Advances in Neural Information Processing Systems, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, eds. (Curran Associates, 2012), Vol. 25, pp. 341–349.
L. Huang, L. Xu, and A. E. Miroshnichenko, “Deep learning enabled nanophotonics,” in Advances in Deep Learning (InTech, 2020).
[Crossref]
W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, “Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy,” Adv. Mater. 31, 1901111 (2019).
[Crossref]
J. Zhou, B. Huang, Z. Yan, and J.-C. G. Bünzli, “Emerging role of machine learning in light-matter interaction,” Light Sci. Appl. 8, 1 (2019).
[Crossref]
M. Elzouka, C. Yang, A. Albert, S. Lubner, and R. S. Prasher, “Interpretable inverse design of particle spectral emissivity using machine learning,” arXiv:2002.04223 (2020).
J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13, 8872–8878 (2019).
[Crossref]
J. Trisno, H. Wang, H. T. Wang, R. J. H. Ng, S. D. Rezaei, and J. K. W. Yang, “Applying machine learning to the optics of dielectric nano-blobs,” Adv. Photonics Res. 1, 2000068 (2020).
[Crossref]
J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4, eaar4206 (2018).
[Crossref]
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
R. Unni, K. Yao, and Y. Zheng, “Deep convolutional mixture density network for inverse design of layered photonic structures,” ACS Photonics 7, 2703–2712 (2020).
[Crossref]
K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” Nanophotonics 8, 339–366 (2019).
[Crossref]
X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).
[Crossref]
M. Raissi, A. Yazdani, and G. E. Karniadakis, “Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations,” Science 367, 1026–1030 (2020).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, B. Pham, J. Liang, D. Ho, M. W. Knight, and A. P. Raman, “Designing multiplexed supercell metasurfaces with tandem neural networks,” Nanophotonics 10, 1133–1143 (2021).
[Crossref]
C. Yeung, J.-M. Tsai, B. King, Y. Kawagoe, D. Ho, M. Knight, and A. P. Raman, “Elucidating the behavior of nanophotonic structures through explainable machine learning algorithms,” ACS Photonics 7, 2309–2318 (2020).
[Crossref]
C. Yeung, J.-M. Tsai, Y. Kawagoe, B. King, D. Ho, and A. P. Raman, “Elucidating the design and behavior of nanophotonic structures through explainable convolutional neural networks,” arXiv:2003.06075 (2020).
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M.-H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]
C. You, M. A. Quiroz-Juárez, A. Lambert, N. Bhusal, C. Dong, A. Perez-Leija, A. Javaid, R. de. J. León-Montiel, and O. S. Magaña-Loaiza, “Identification of light sources using machine learning,” Appl. Phys. Rev. 7, 021404 (2020).
[Crossref]
S. Wang, K. Fan, N. Luo, Y. Cao, F. Wu, C. Zhang, K. A. Heller, and L. You, “Massive computational acceleration by using neural networks to emulate mechanism-based biological models,” Nat. Commun. 10, 4354 (2019).
[Crossref]
D. Piccinotti, K. F. MacDonald, S. Gregory, I. Youngs, and N. I. Zheludev, “Artificial intelligence for photonics and photonic materials,” Rep. Prog. Phys. 84, 012401 (2020).
[Crossref]
A. Youssry, R. J. Chapman, A. Peruzzo, C. Ferrie, and M. Tomamichel, “Modeling and control of a reconfigurable photonic circuit using deep learning,” Quantum Sci. Technol. 5, 025001 (2020).
[Crossref]
C. Zhang, J. Jin, W. Na, Q.-J. Zhang, and M. Yu, “Multivalued neural network inverse modeling and applications to microwave filters,” IEEE Trans. Microwave Theory Tech. 66, 3781–3797 (2018).
[Crossref]
H. Kabir, Y. Wang, M. Yu, and Q.-J. Zhang, “Neural network inverse modeling and applications to microwave filter design,” IEEE Trans. Microwave Theory Tech. 56, 867–879 (2008).
[Crossref]
E. Ashalley, K. Acheampong, L. V. Besteiro, L. V. Besteiro, P. Yu, A. Neogi, A. O. Govorov, A. O. Govorov, and Z. M. Wang, “Multitask deep-learning-based design of chiral plasmonic metamaterials,” Photon. Res. 8, 1213–1225 (2020).
[Crossref]
L. Gao, X. Li, D. Liu, L. Wang, and Z. Yu, “A bidirectional deep neural network for accurate silicon color design,” Adv. Mater. 31, 1905467 (2019).
[Crossref]
D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5, 1365–1369 (2018).
[Crossref]
T. Pu, J.-Y. Ou, V. Savinov, G. Yuan, N. Papasimakis, and N. Zheludev, “Unlabeled far-field deeply subwavelength topological microscopy (DSTM),” Adv. Sci. 8, 2002886 (2020).
[Crossref]
A. M. Palmieri, E. Kovlakov, F. Bianchi, D. Yudin, S. Straupe, J. D. Biamonte, and S. Kulik, “Experimental neural network enhanced quantum tomography,” npj Quantum Inf. 6, 20 (2020).
[Crossref]
Y. Zhu, N. Zabaras, P.-S. Koutsourelakis, and P. Perdikaris, “Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data,” J. Comput. Phys. 394, 56–81 (2019).
[Crossref]
Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, and A. Adibi, “Knowledge discovery in nanophotonics using geometric deep learning,” Adv. Intell. Syst. 2, 1900132 (2020).
[Crossref]
Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theor. Simul. 2, 1900088 (2019).
[Crossref]
F. Zangeneh-Nejad, D. L. Sounas, A. Alù, and R. Fleury, “Analogue computing with metamaterials,” Nat. Rev. Mater. 6, 207–225 (2021).
[Crossref]
M. Kauranen and A. V. Zayats, “Nonlinear plasmonics,” Nat. Photonics 6, 737–748 (2012).
[Crossref]
A. Ghosh, D. J. Roth, L. H. Nicholls, W. P. Wardley, A. V. Zayats, and V. A. Podolskiy, “Machine learning—based diffractive imaging with subwavelength resolution,” arXiv:2005.03595 (2020).
M. Krenn, M. Erhard, and A. Zeilinger, “Computer-inspired quantum experiments,” Nat. Rev. Phys. 2, 649–661 (2020).
[Crossref]
A. A. Melnikov, H. P. Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, and H. J. Briegel, “Active learning machine learns to create new quantum experiments,” Proc. Natl. Acad. Sci. USA 115, 1221–1226 (2018).
[Crossref]
M. Krenn, M. Malik, R. Fickler, R. Lapkiewicz, and A. Zeilinger, “Automated search for new quantum experiments,” Phys. Rev. Lett. 116, 090405 (2016).
[Crossref]
Z. Fang and J. Zhan, “Deep physical informed neural networks for metamaterial design,” IEEE Access 8, 24506–24513 (2020).
[Crossref]
S. Wang, K. Fan, N. Luo, Y. Cao, F. Wu, C. Zhang, K. A. Heller, and L. You, “Massive computational acceleration by using neural networks to emulate mechanism-based biological models,” Nat. Commun. 10, 4354 (2019).
[Crossref]
C. Zhang, J. Jin, W. Na, Q.-J. Zhang, and M. Yu, “Multivalued neural network inverse modeling and applications to microwave filters,” IEEE Trans. Microwave Theory Tech. 66, 3781–3797 (2018).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
S. An, B. Zheng, H. Tang, M. Y. Shalaginov, L. Zhou, H. Li, T. Gu, J. Hu, C. Fowler, and H. Zhang, “Multifunctional metasurface design with a generative adversarial network,” arXiv:1908.04851 (2020).
P. Zhang, S. Liu, A. Chaurasia, D. Ma, M. J. Mlodzianoski, E. Culurciello, and F. Huang, “Analyzing complex single-molecule emission patterns with deep learning,” Nat. Methods 15, 913–916 (2018).
[Crossref]
C. Zhang, J. Jin, W. Na, Q.-J. Zhang, and M. Yu, “Multivalued neural network inverse modeling and applications to microwave filters,” IEEE Trans. Microwave Theory Tech. 66, 3781–3797 (2018).
[Crossref]
H. Kabir, Y. Wang, M. Yu, and Q.-J. Zhang, “Neural network inverse modeling and applications to microwave filter design,” IEEE Trans. Microwave Theory Tech. 56, 867–879 (2008).
[Crossref]
Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]
Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydn, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2018).
[Crossref]
Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4, 1437–1443 (2017).
[Crossref]
X. Li, J. Dong, B. Li, Y. Zhang, Y. Zhang, A. Veeraraghavan, and X. Ji, “Fast confocal microscopy imaging based on deep learning,” in IEEE International Conference on Computational Photography (ICCP) (2020), pp. 1–12.
X. Li, J. Dong, B. Li, Y. Zhang, Y. Zhang, A. Veeraraghavan, and X. Ji, “Fast confocal microscopy imaging based on deep learning,” in IEEE International Conference on Computational Photography (ICCP) (2020), pp. 1–12.
X. Shi, T. Qiu, J. Wang, X. Zhao, and S. Qu, “Metasurface inverse design using machine learning approaches,” J. Phys. D 53, 275105 (2020).
[Crossref]
T. Pu, J.-Y. Ou, V. Savinov, G. Yuan, N. Papasimakis, and N. Zheludev, “Unlabeled far-field deeply subwavelength topological microscopy (DSTM),” Adv. Sci. 8, 2002886 (2020).
[Crossref]
T. Pu, J. Y. Ou, N. Papasimakis, and N. I. Zheludev, “Label-free deeply subwavelength optical microscopy,” Appl. Phys. Lett. 116, 131105 (2020).
[Crossref]
D. Piccinotti, K. F. MacDonald, S. Gregory, I. Youngs, and N. I. Zheludev, “Artificial intelligence for photonics and photonic materials,” Rep. Prog. Phys. 84, 012401 (2020).
[Crossref]
M. V. Zhelyeznyakov, S. L. Brunton, and A. Majumdar, “Deep learning to accelerate Maxwell’s equations for inverse design of dielectric metasurfaces,” arXiv:2008.10632 (2020).
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
S. An, B. Zheng, H. Tang, M. Y. Shalaginov, L. Zhou, H. Li, T. Gu, J. Hu, C. Fowler, and H. Zhang, “Multifunctional metasurface design with a generative adversarial network,” arXiv:1908.04851 (2020).
R. Unni, K. Yao, and Y. Zheng, “Deep convolutional mixture density network for inverse design of layered photonic structures,” ACS Photonics 7, 2703–2712 (2020).
[Crossref]
K. Yao, R. Unni, and Y. Zheng, “Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale,” Nanophotonics 8, 339–366 (2019).
[Crossref]
H. Wang, Z. Zheng, C. Ji, and L. J. Guo, “Automated multi-layer optical design via deep reinforcement learning,” Mach. Learn. Sci. Technol. 2, 025013 (2021).
[Crossref]
H. Wang, Z. Zheng, C. Ji, and L. J. Guo, “Automated multi-layer optical design via deep reinforcement learning,” Mach. Learn. Sci. Technol. (2020).
J. Zhou, B. Huang, Z. Yan, and J.-C. G. Bünzli, “Emerging role of machine learning in light-matter interaction,” Light Sci. Appl. 8, 1 (2019).
[Crossref]
S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, and H. Zhang, “Deep learning modeling approach for metasurfaces with high degrees of freedom,” Opt. Express 28, 31932–31942 (2020).
[Crossref]
S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, and H. Zhang, “A deep learning approach for objective-driven all-dielectric metasurface design,” ACS Photonics 6, 3196–3207 (2019).
[Crossref]
S. An, B. Zheng, H. Tang, M. Y. Shalaginov, L. Zhou, H. Li, T. Gu, J. Hu, C. Fowler, and H. Zhang, “Multifunctional metasurface design with a generative adversarial network,” arXiv:1908.04851 (2020).
B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, and J. Kong, “Deep learning enabled fast optical characterization of two-dimensional materials,” arXiv:1906.11220 (2019).
Z. Liu, L. Raju, D. Zhu, and W. Cai, “A hybrid strategy for the discovery and design of photonic structures,” IEEE J. Emerging Sel. Top. Circuits Syst. 10, 126–135 (2020).
[Crossref]
Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18, 6570–6576 (2018).
[Crossref]
D. Z. Zhu, E. B. Whiting, S. D. Campbell, D. B. Burckel, and D. H. Werner, “Optimal high efficiency 3D plasmonic metasurface elements revealed by lazy ants,” ACS Photonics 6, 2741–2748 (2019).
[Crossref]
S. D. Campbell, D. Z. Zhu, E. B. Whiting, J. Nagar, D. H. Werner, and P. L. Werner, “Advanced multi-objective and surrogate-assisted optimization of topologically diverse metasurface architectures,” Proc. SPIE 10719, 107190U (2018).
[Crossref]
Y.-T. Luo, P.-Q. Li, D.-T. Li, Y.-G. Peng, Z.-G. Geng, S.-H. Xie, Y. Li, A. Alù, J. Zhu, and X.-F. Zhu, “Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures,” Research 2020, 8757403 (2020).
[Crossref]
Y.-T. Luo, P.-Q. Li, D.-T. Li, Y.-G. Peng, Z.-G. Geng, S.-H. Xie, Y. Li, A. Alù, J. Zhu, and X.-F. Zhu, “Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures,” Research 2020, 8757403 (2020).
[Crossref]
Y. Zhu, N. Zabaras, P.-S. Koutsourelakis, and P. Perdikaris, “Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data,” J. Comput. Phys. 394, 56–81 (2019).
[Crossref]
M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).
[Crossref]
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proceedings of Advances in Neural Information Processing System (2020), pp. 1877–1901.
W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36, 460–468 (2018).
[Crossref]
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