Hong-Gang Sun

DNNsolver: Accurate and Efficient Deep Learning Modeling of Polarization-Sensitive Diffractive Neural Networks

DNNsolver is proposed, a deep learning-based electromagnetic surrogate model that learns the scattering response of diffractive layers by predicting the scattering matrix rather than source-dependent output fields, which decouples the incident wavefront from the model and could be applied to arbitrary input fields.