Machine learning to enhance the calculation of optical forces in the geometrical optics approximation

David Bronte Ciriza; Alessandro Magazzù; Agnese Callegari; Maria A. Iatì; Giovanni Volpe; Onofrio M. Maragò

doi:10.1364/OMA.2021.AF2D.3

Biophotonics Congress 2021
OSA Technical Digest (Optica Publishing Group, 2021),
paper AF2D.3
•https://doi.org/10.1364/OMA.2021.AF2D.3

Machine learning to enhance the calculation of optical forces in the geometrical optics approximation

David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Maria A. Iatì, Giovanni Volpe, and Onofrio M. Maragò

Optical Manipulation and Its Applications 2021

Washington, DC United States
12–16 April 2021
ISBN: 978-1-943580-85-9

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Optical Tweezers Applications and New Analysis Tools (AF2D)

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D. B. Ciriza, A. Magazzù, A. Callegari, M. A. Iatì, G. Volpe, and O. M. Maragò, "Machine learning to enhance the calculation of optical forces in the geometrical optics approximation," in Biophotonics Congress 2021, C. Boudoux, K. Maitland, C. Hendon, M. Wojtkowski, K. Quinn, M. Schanne-Klein, N. Durr, D. Elson, F. Cichos, L. Oddershede, V. Emiliani, O. Maragò, S. Nic Chormaic, N. Pégard, S. Gibbs, S. Vinogradov, M. Niedre, K. Samkoe, A. Devor, D. Peterka, P. Blinder, and E. Buckley, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper AF2D.3.

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Abstract

We show how machine learning can improve the speed and accuracy of the optical force calculations in the geometrical optics approximation.

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