Fast Physics-Driven Untrained Network for Highly Nonlinear Inverse Scattering Problems

• URL: http://arxiv.org/abs/2602.13805v1
• Date: Sat, 14 Feb 2026 14:30:12 GMT
• Title: Fast Physics-Driven Untrained Network for Highly Nonlinear Inverse Scattering Problems
• Authors: Yutong Du, Zicheng Liu, Yi Huang, Bazargul Matkerim, Bo Qi, Yali Zong, Peixian Han,
• Abstract summary: Untrained neural networks (UNNs) offer high-fidelity electromagnetic inverse scattering reconstruction.<n>We propose a Real-Time Physics-Driven Fourier-Spectral (PDF) solver that achieves sub-second reconstruction through spectral-domain dimensionality reduction.<n>Results demonstrate a 100-fold speedup over state-of-the-art UNNs with robust performance under noise and antenna uncertainties.
• Score: 12.21924310599522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Untrained neural networks (UNNs) offer high-fidelity electromagnetic inverse scattering reconstruction but are computationally limited by high-dimensional spatial-domain optimization. We propose a Real-Time Physics-Driven Fourier-Spectral (PDF) solver that achieves sub-second reconstruction through spectral-domain dimensionality reduction. By expanding induced currents using a truncated Fourier basis, the optimization is confined to a compact low-frequency parameter space supported by scattering measurements. The solver integrates a contraction integral equation (CIE) to mitigate high-contrast nonlinearity and a contrast-compensated operator (CCO) to correct spectral-induced attenuation. Furthermore, a bridge-suppressing loss is formulated to enhance boundary sharpness between adjacent scatterers. Numerical and experimental results demonstrate a 100-fold speedup over state-of-the-art UNNs with robust performance under noise and antenna uncertainties, enabling real-time microwave imaging applications.

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