Physics-Informed Neural Operator for Solving Electromagnetic Forward and Inverse Scattering Problems

A framework based on a physics-informed neural operator is proposed to solve electromagnetic forward and inverse scattering problems. By embedding the governing physical equations directly into the learning architecture, the proposed method achieves high accuracy with fast inference while eliminating the need for labelled data. Numerical experiments validate the effectiveness of the proposed framework.