Optical design using physics-inspired neural networks

Demetri Psaltis; Carlo Gigli; Amirhossein Saba

doi:10.1117/12.2679815

12 August 2023 Optical design using physics-inspired neural networks

Demetri Psaltis, Carlo Gigli, Amirhossein Saba

Proceedings Volume PC12622, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI; PC126220I (2023) https://doi.org/10.1117/12.2679815
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

We will describe MaxwellNet, a machine learning method for analyzing and designing optical systems using Maxwell's equations as the loss function in the optimisation process. We will describe results of the application of MaxwellNet to nanophotonics, nonlinear optics and optical diffraction tomography

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Citation Download Citation

Demetri Psaltis, Carlo Gigli, and Amirhossein Saba "Optical design using physics-inspired neural networks", Proc. SPIE PC12622, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI, PC126220I (12 August 2023); https://doi.org/10.1117/12.2679815

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KEYWORDS

Optical design

Neural networks

Physics

Nonlinear optics

Diffraction

Machine learning

Maxwell equations

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