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Elucidating the Physics of Nanophotonic Structures Through Explainable Machine Learning Algorithms

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Abstract

Explainable machine learning algorithms were applied to convolutional neural networks to reveal deeper insights into the properties of metamaterials, demonstrating new avenues for physics discovery and device optimization in optics and photonics.

© 2020 The Author(s)

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More Like This
Conditional Machine Learning-Based Inverse Design Across Multiple Classes of Nanophotonic Structures

Christopher Yeung, Ryan Tsai, Benjamin Pham, Brian King, Yusaku Kawagoe, David Ho, Julia Liang, Mark W. Knight, and Aaswath Raman
AW3E.6 CLEO: QELS_Fundamental Science (CLEO_QELS) 2021

On the Application of Explainable Artificial Intelligence to Lightpath QoT Estimation

Omran Ayoub, Andrea Bianco, Davide Andreoletti, Sebastian Troia, Silvia Giordano, and Cristina Rottondi
M3F.5 Optical Fiber Communication Conference (OFC) 2022

Recurrent Machine Learning and Computing with Nonlinear Optical Waves

Ian A. D. Williamson, Tyler W. Hughes, Momchil Minkov, and Shanhui Fan
FW4B.1 CLEO: QELS_Fundamental Science (CLEO_QELS) 2020

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