Coordinate-Residual Physics-Driven Neural Network for Inverse Scattering Imaging
Electromagnetic inverse scattering is a nonlinear and ill-posed computational imaging problem, where accurate reconstruction is challenging due to measurement limitations, noise, and high computational costs, especially for 3-D imaging. Although physics-driven neural networks (PDNNs) reduce the dependence on labeled training data, existing accelerated PDNN frameworks often rely on preliminary reconstruction-based region selection, which may introduce instability when the selected region is inaccurate. In this paper, a coordinate-residual physics-driven neural network (CRPDNN) is proposed for 3-D electromagnetic inverse scattering. CRPDNN represents the unknown complex contrast distributio...
| Authors | Yutong Du, Zicheng Liu, Bo Qi et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 10 August 2026 |
| Last updated at source | 1 September 2026 |
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