Coordinate-Residual Physics-Driven Neural Network for Electromagnetic Inverse Scattering

• URL: http://arxiv.org/abs/2608.09382v1
• Date: Mon, 10 Aug 2026 09:57:57 GMT
• Title: Coordinate-Residual Physics-Driven Neural Network for Electromagnetic Inverse Scattering
• Abstract summary: A coordinate-residual physics-driven neural network (CRPDNN) is proposed for 3-D electromagnetic inverse scattering.<n>CRPDNN achieves an average relative error of 2.10%, compared with 7.97% for CSI and 3.99% for $L_2/3$-FBE-WCIE.
• Score: 8.620366797105282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electromagnetic inverse scattering is a nonlinear and ill-posed 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. The proposed solver directly reconstructs the unknown contrast distribution using normalized spatial coordinates and a residual convolutional network, without requiring a preliminary reconstruction. For the reported noise-free 3-D synthetic cases, CRPDNN achieves an average relative error of 2.10\%, compared with 7.97\% for CSI and 3.99\% for $L_{2/3}$-FBE-WCIE, while providing approximately 5.5- and 12.1-fold speedups over the two baselines, respectively. Supplementary 2-D comparisons further confirm its stability and computational efficiency relative to existing PDNN frameworks. CRPDNN also maintains reliable reconstruction performance under noisy measurements, and the 3-D Fresnel experiments further indicate its potential for practical imaging applications. The related code is available at https://github.com/Physics-driven-methods.

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