54 citations · 115 across the 3 of their papers we have counts for
3 papers
physics.comp-ph2021★ 47 cited
Neural Born Iteration Method For Solving Inverse Scattering Problems: 2D Cases
Tao Shan, Zhichao Lin, Xiaoqian Song +3
In this paper, we propose the neural Born iterative method (NeuralBIM) for solving 2D inverse scattering problems (ISPs) by drawing on the scheme of physics-informed supervised res…
physics.comp-ph2021★ 54 cited
Physics-Informed Supervised Residual Learning for Electromagnetic Modeling
Tao Shan, Jinhong Zeng, Xiaoqian Song +4
In this study, physics-informed supervised residual learning (PhiSRL) is proposed to enable an effective, robust, and general deep learning framework for 2D electromagnetic (EM) mo…
physics.comp-ph2017★ 14 cited
Study on a Poisson's Equation Solver Based On Deep Learning Technique
Tao Shan, Wei Tang, Xunwang Dang +4
In this work, we investigated the feasibility of applying deep learning techniques to solve Poisson's equation. A deep convolutional neural network is set up to predict the distrib…