Unified, Verifiable Neural Simulators for Electromagnetic Wave Inverse Problems

arXiv:2404.00545v1 Announce Type: cross
Abstract: Simulators based on neural networks offer a path to orders-of-magnitude faster electromagnetic wave simulations. Existing models, however, only address narrowly tailored classes of problems and only scale to systems of a few dozen degrees of freedom (DoFs). Here, we demonstrate a single, unified model capable of addressing scattering simulations with thousands of DoFs, of any wavelength, any illumination wavefront, and freeform materials, within broad configurable bounds. Based on an attentional multi-conditioning strategy, our method also …

abstract arxiv cs.lg eess.iv faster freedom however networks neural networks orders path physics.optics scale simulations systems type unified model