# Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems

arXiv: https://arxiv.org/abs/2507.16321 · HTML: https://mlsanity.com/paper/2507.16321/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 22 Jul 2025.

## 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)
- [Neural Born Iteration Method For Solving Inverse Scattering Problems: 2D Cases](https://mlsanity.com/paper/2112.09831/) (2021)
- [3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion](https://mlsanity.com/paper/2305.05425/) (2023)

## Built on by (most cited first, 5 of 5)

- [Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems](https://mlsanity.com/paper/2512.09333/) (2025)
- [Fast Physics-Driven Untrained Network for Highly Nonlinear Inverse Scattering Problems](https://mlsanity.com/paper/2602.13805/) (2026)
- [Contrast-Source-Based Physics-Driven Neural Network for Inverse Scattering Problems](https://mlsanity.com/paper/2601.19243/) (2026)
- [Coordinate-Residual Physics-Driven Neural Network for Inverse Scattering Imaging](https://mlsanity.com/paper/2608.09382/) (2026)
- [Structured Multi-Level-Set Inversion for 3-D Electromagnetic Inverse Scattering](https://mlsanity.com/paper/2609.08594/) (2026)

## Abstract (arXiv)

In recent years, deep learning-based methods have been proposed for solving inverse scattering problems (ISPs), but most of them heavily rely on data and suffer from limited generalization capabilities. In this paper, a new solving scheme is proposed where the solution is iteratively updated following the updating of the physics-driven neural network (PDNN), the hyperparameters of which are optimized by minimizing the loss function which incorporates the constraints from the collected scattered fields and the prior information about scatterers. Unlike data-driven neural network solvers, PDNN is trained only requiring the input of collected scattered fields and the computation of scattered fields corresponding to predicted solutions, thus avoids the generalization problem. Moreover, to accelerate the imaging efficiency, the subregion enclosing the scatterers is identified. Numerical and experimental results demonstrate that the proposed scheme has high reconstruction accuracy and strong stability, even when dealing with composite lossy scatterers.