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cs.LG2026
Structured Multi-Level-Set Inversion for 3-D Electromagnetic Inverse Scattering
Yutong Du, Zicheng Liu, Bo Qi +3
Many dielectric targets encountered in 3-D electromagnetic inverse scattering exhibit piecewise-homogeneous material distributions, which provide useful structural information for…
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…
cs.LG2025
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…