deep learning 1electromagnetic scattering 1photonic crystals 1physics-informed neural networks 1symmetry 1
From the 1 of 2 linked papers with an AI index.
2 papers
physics.optics2026
Symmetry-Informed Deep Learning for Electromagnetic Scattering
Viktor A. Lilja, Philippe Tassin
The paper introduces a symmetry‑aware deep learning framework that uses the equivariance of Maxwell’s equations to create data‑efficient and physically consistent surrogate models…
physics.optics2026
A general framework for knowledge integration in machine learning for electromagnetic scattering using quasinormal modes
Viktor A. Lilja, Albin J. Svärdsby, Timo Gahlmann +1
Neural networks have been demonstrated to be able to accelerate the modeling and inverse design of optical and electromagnetic devices by serving as fast surrogates for electromagn…