A neural operator-based surrogate solver for free-form electromagnetic inverse design
This work addresses a domain-specific problem in nanophotonics, enabling inverse design of complex electromagnetic structures that were previously inaccessible to deep learning techniques.
The authors tackled the problem of electromagnetic scattering and nanophotonic inverse design by implementing a modified Fourier neural operator as a surrogate solver, achieving data efficiency improvements compared to existing methods and enabling gradient-based design of free-form 3D scatterers.
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.