Predicting nonlinear optical scattering with physics informed neural networks

Carlo Gigli; Amirhossein Saba; Ahmed Bassam Ayoub; Demetri Psaltis

doi:10.1117/12.2651859

17 March 2023 Predicting nonlinear optical scattering with physics informed neural networks

Carlo Gigli, Amirhossein Saba, Ahmed Bassam Ayoub, Demetri Psaltis

Proceedings Volume PC12438, AI and Optical Data Sciences IV; PC124380Q (2023) https://doi.org/10.1117/12.2651859
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

Deep neural network trained on physical losses are emerging as promising surrogates of nonlinear numerical solvers. These tools can predict solutions of Maxwell’s equations and compute gradients of output fields with respect to material properties in millisecond times which makes them very attractive for inverse design or inverse scattering applications. Here we demonstrate a neural network able to compute light scattering from inhomogeneous media in the presence of the optical Kerr effect from glass diffusers with a size comparable with the incident wavelength. The weights of the network are dynamically adjusted to take into account the intensity dependent refractive index of the material.

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Citation Download Citation

Carlo Gigli, Amirhossein Saba, Ahmed Bassam Ayoub, and Demetri Psaltis "Predicting nonlinear optical scattering with physics informed neural networks", Proc. SPIE PC12438, AI and Optical Data Sciences IV, PC124380Q (17 March 2023); https://doi.org/10.1117/12.2651859

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