Physics Embedded Deep Neural Network for Solving Full-Wave Inverse Scattering Problems
Rui Guo, Zhichao Lin, Tao Shan, Xiaoqian Song, Maokun Li, Fan Yang, Shenheng Xu, Aria Abubakar
Abstract
In this work, we design an iterative deep neural network to solve full-wave inverse scattering problems (ISPs) in the 2-D case. Forward modeling neural networks that predict the scattered field are embedded in an inversion neural network. In an iterative manner, the inversion network predicts the model update from the residual between the simulated data and the observed data. The proposed inversion network can achieve super-resolution reconstruction meanwhile keeping the simulated data of reconstructed models well consistent with the observed data. We validate this method with both synthetic and experimental data inversion. Results show that the inversion network can predict models with high accuracy, efficiency, and good generalization ability. By combining deep learning and physical simulation together, the proposed method provides a way for real-time imaging with high reliability and accuracy.