54 citations · 147 across the 28 of their papers we have counts for
10 papers · 1 filter
Attenuation Compensation in Lossy Media via the Wave Operator Model
Tianchen Shao, Zekui Jia, Maokun Li +2
The wave operator model provides a framework for modeling wave propagation by encoding material parameter distributions into matrix-form operators. This paper extends this framewor…
Anti-Interference Communication Using Computational Antenna
Xiaocun Zong, Fan Yang, Shenheng Xu +1
This letter proposes a novel anti-interference communication method leveraging computational antennas, utilizing time averaging and 1-bit reconfigurable intelligent surfaces (RIS)…
A Differentiable Framework for Full and Phaseless Data Inversion Using Neural Implicit Contrast-Source Representation
Haoran Sun, Daoqi Liu, Hongyu Zhou +3
In this study, we extend the contrast source inversion to a fully differentiable, unsupervised framework based on a neural implicit representation of the contrast source. Specifica…
Multi-frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems
Daoqi Liu, Tao Shan, Maokun Li +2
In this work, we propose a deep learning-based imaging method for addressing the multi-frequency electromagnetic (EM) inverse scattering problem (ISP). By combining deep learning t…
Analysis of Degrees of Freedom in Scattered Fields for Nonlinear Inverse Scattering Problems
Zhichao Lin, Rui Guo, Tao Shan +4
In the study of nonlinear inverse scattering problems (ISPs), the traditional method for estimating the number of degrees of freedom (NDoF) of the scattered field does not consider…
Physics Embedded Machine Learning for Electromagnetic Data Imaging
Rui Guo, Tianyao Huang, Maokun Li +2
Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usuall…
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-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…
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…
Nonuniform metasurface analysis in FDTD Based on Susceptibility GSTCs
Xiao Jia, Maokun Li, Shenheng Xu +2
An accelerated analysis method for non-uniform metasurfaces in FDTD is presented for the first time. The non-uniform metasurfaces are modeled by a distribution of susceptibilities,…