Physics-informed Neural Operator for Solving Electromagnetic Forward and Inverse Problems

A physics-informed Fourier neural operator is proposed to solve both electromagnetic forward and inverse scattering problems. By embedding the governing physical equations directly into the learning architecture, the method achieves fast speed, high accuracy and strong generalization, while removing the need for labelled data. Numerical experiments demonstrate and validate the effectiveness of the proposed framework.