# Contrast-Source-Based Physics-Driven Neural Network for Inverse Scattering Problems

arXiv: https://arxiv.org/abs/2601.19243 · HTML: https://mlsanity.com/paper/2601.19243/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Builds on (most cited first, 5 of 5)

- [DeepNIS: Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering](https://mlsanity.com/paper/1810.03990/) (2018)
- [Physics-guided Loss Functions Improve Deep Learning Performance in Inverse Scattering](https://mlsanity.com/paper/2111.09109/) (2021)
- [SOM-Net: Unrolling the Subspace-based Optimization for Solving Full-wave Inverse Scattering Problems](https://mlsanity.com/paper/2209.03567/) (2022)
- [Quality-factor inspired deep neural network solver for solving inverse scattering problems](https://mlsanity.com/paper/2504.20504/) (2025)
- [Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems](https://mlsanity.com/paper/2507.16321/) (2025)

## Abstract (arXiv)

Deep neural networks (DNNs) have recently been applied to inverse scattering problems (ISPs) due to their strong nonlinear mapping capabilities. However, supervised DNN solvers require large-scale datasets, which limits their generalization in practical applications. Untrained neural networks (UNNs) address this issue by updating weights from measured electric fields and prior physical knowledge, but existing UNN solvers suffer from long inference time. To overcome these limitations, this paper proposes a contrast-source-based physics-driven neural network (CSPDNN), which predicts the induced current distribution to improve efficiency and incorporates an adaptive total variation loss for robust reconstruction under varying contrast and noise conditions. The improved imaging performance is validated through comprehensive numerical simulations and experimental data.