Push the Generalization Limitation of Learning Approaches by Multidomain Weight-Sharing for Full-Wave Inverse Scattering
Yusong Wang, Zheng Zong, Siyuan He, Rencheng Song, Zhun Wei
Abstract
Recently, deep learning approaches have shown their advantages on solving scientific problems including full-wave nonlinear inverse problems. However, these data-driven methods face severe problems, especially about the low generalization ability, which means the trained models only work for scenarios with similar training data and physical setups. In this work, we propose a multi-domain weight-sharing method (MDWS) for inverse scattering problems, which increases the generalization ability of learning approaches for both data-based and physical-based out-of-range tests. Specifically, the proposed MDWS utilizes a physical layer of Green’s function to transform between induced current domain and electrical field domain, where weight-sharing blocks having the same weights in different incidences and stages are used to decouple the network structure from measurement setups. It is shown by intensive numerical and experimental tests both qualitatively and quantitatively that the proposed MDWS apparently outperforms the benchmarked method. Further, the proposed weight-sharing architecture also provides an efficient way to build large model in electromagnetic society with much less memory and computational cost.