3 papers
physics.optics2024
Multiscale Physics-Informed Neural Networks for the Inverse Design of Hyperuniform Optical Materials
Roberto Riganti, Yilin Zhu, Wei Cai +2
In this article, we employ multiscale physics-informed neural networks (MscalePINNs) for the inverse design of finite-size photonic materials with stealthy hyperuniform (SHU) disor…
physics.optics2023
Field theory description of the non-perturbative optical nonlinearity of epsilon-near-zero media
Yaraslau Tamashevich, Tornike Shubitidze, Luca Dal Negro +1
In this paper we introduce a fully non-perturbative approach for the description of the optical nonlinearity of epsilon-near-zero (ENZ) media. In particular, based on the rigorous…
cond-mat.dis-nn2023
Auxiliary Physics-Informed Neural Networks for Forward, Inverse, and Coupled Radiative Transfer Problems
Roberto Riganti, Luca Dal Negro
In this paper, we develop and employ auxiliary physics-informed neural networks (APINNs) to solve forward, inverse, and coupled integro-differential problems of radiative transfer…