Science University Research Symposium (SURS)

Physics-Informed Fourier Neural Operators for Nanophotonic Scattering in Nonlinear Media

Publication Date

Fall 11-24-2025

College

College of Sciences & Mathematics

Department

Chemistry and Physics, Department of

SURS Faculty Advisor

Scott Hawley

Presentation Type

Poster Presentation

Abstract

The Fourier Neural Operator (FNO) has shown remarkable success in approximating solutions to partial differential equations in fluid dynamics, yet its potential for nanophotonic systems remains largely unexplored. We present a physics-informed FNO architecture for solving Maxwell's equations for scattering problems containing nonlinear materials. Trained on finite-difference time-domain (FDTD) simulations, we develop a model that learns the family of solutions mapping electromagnetic field configurations forward in time. Additionally, we construct a model that can predict the optical spectra given initial conditions. We benchmark both approaches against conventional FDTD and finite-element solvers to assess computational efficiency and accuracy. Critically, we explore how well the trained models generalize beyond their training domains, predicting fields and spectra for untested resolutions, geometric configurations, and source parameters. These results suggest FNOs could dramatically accelerate the inverse design and optimization workflows for nonlinear photonic metasurfaces, where conventional solvers remain prohibitively expensive for exploring large parameter spaces.

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