4 papers
Multi-Level-Set-Based Physics-Driven Neural Network to Solve 3-D Inverse Scattering Problems
Yutong Du, Zicheng Liu, Bo Qi +2
This paper proposes a level-set-based physics-driven neural network solver (LSPDNN) for 3-D electromagnetic inverse scattering. To mitigate boundary blurring and reconstruction art…
Coordinate-Residual Physics-Driven Neural Network for Inverse Scattering Imaging
Yutong Du, Zicheng Liu, Bo Qi +2
Electromagnetic inverse scattering is a nonlinear and ill-posed computational imaging problem, where accurate reconstruction is challenging due to measurement limitations, noise, a…
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…
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…