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

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...

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Bibliographic Details
Published in:arXiv.org 2023-03
Main Authors: Augenstein, Yannick, Repän, Taavi, Rockstuhl, Carsten
Format: Article
Language:English
Subjects:
Deep learning
Electromagnetic scattering
Free form
Inverse design
Machine learning
Partial differential equations
Solvers
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2302.01934