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

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.

References in corpus (2)

• On the use of Artificial Neural Networks in Topology Optimisation
• Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets