5 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…
Field Reconstruction for High-Frequency Electromagnetic Exposure Assessment Based on Deep Learning
Miao Cao, Zicheng Liu, Bazargul Matkerim +4
Fifth-generation (5G) communication systems, operating in higher frequency bands from 3 to 300 GHz, provide unprecedented bandwidth to enable ultra-high data rates and low-latency…
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…