6 papers
Coordinate-Residual Physics-Driven Neural Network for Electromagnetic Inverse Scattering
Yutong Du, Zicheng Liu, Bo Qi +2
Electromagnetic inverse scattering is a nonlinear and ill-posed problem, where accurate reconstruction is challenging due to measurement limitations, noise, and high computational…
Fast Physics-Driven Untrained Network for Highly Nonlinear Inverse Scattering Problems
Yutong Du, Zicheng Liu, Yi Huang +4
Untrained neural networks (UNNs) offer high-fidelity electromagnetic inverse scattering reconstruction but are computationally limited by high-dimensional spatial-domain optimizati…
Contrast-Source-Based Physics-Driven Neural Network for Inverse Scattering Problems
Yutong Du, Zicheng Liu
Deep neural networks (DNNs) have recently been applied to inverse scattering problems (ISPs) due to their strong nonlinear mapping capabilities. However, supervised DNN solvers req…
Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems
Yutong Du, Zicheng Liu, Bo Wu +5
This paper presents an improved physics-driven neural network (IPDNN) framework for solving electromagnetic inverse scattering problems (ISPs). A new Gaussian-localized oscillation…
Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
Yutong Du, Zicheng Liu, Bazargul Matkerim +4
In recent years, deep learning-based methods have been proposed for solving inverse scattering problems (ISPs), but most of them heavily rely on data and suffer from limited genera…
Quality-factor inspired deep neural network solver for solving inverse scattering problems
Yutong Du, Zicheng Liu, Miao Cao +3
Deep neural networks have been applied to address electromagnetic inverse scattering problems (ISPs) and shown superior imaging performances, which can be affected by the training…