A neural operator-based surrogate solver for free-form electromagnetic inverse design

• URL: http://arxiv.org/abs/2302.01934v2
• Date: Tue, 28 Mar 2023 07:22:31 GMT
• Title: A neural operator-based surrogate solver for free-form electromagnetic inverse design
• Authors: Yannick Augenstein, Taavi Rep\"an, Carsten Rockstuhl
• Abstract summary: We implement and train a modified Fourier neural operator as a surrogate solver for electromagnetic scattering problems. We demonstrate its application to the gradient-based nanophotonic inverse design of free-form, fully three-dimensional electromagnetic scatterers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural operators have emerged as a powerful tool for solving partial differential equations in the context of scientific machine learning. Here, we implement and train a modified Fourier neural operator as a surrogate solver for electromagnetic scattering problems and compare its data efficiency to existing methods. We further demonstrate its application to the gradient-based nanophotonic inverse design of free-form, fully three-dimensional electromagnetic scatterers, an area that has so far eluded the application of deep learning techniques.

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