3 papers
cs.LG2026
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…
physics.comp-ph2026
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…
cs.LG2026
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…