# Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems

arXiv: https://arxiv.org/abs/2512.09333 · HTML: https://mlsanity.com/paper/2512.09333/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [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)
- [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)

## Built on by (most cited first, 2 of 2)

- [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)

This paper presents an improved physics-driven neural network (IPDNN) framework for solving electromagnetic inverse scattering problems (ISPs). A new Gaussian-localized oscillation-suppressing window (GLOW) activation function is introduced to stabilize convergence and enable a lightweight yet accurate network architecture. A dynamic scatter subregion identification strategy is further developed to adaptively refine the computational domain, preventing missed detections and reducing computational cost. Moreover, transfer learning is incorporated to extend the solver's applicability to practical scenarios, integrating the physical interpretability of iterative algorithms with the real-time inference capability of neural networks. Numerical simulations and experimental results demonstrate that the proposed solver achieves superior reconstruction accuracy, robustness, and efficiency compared with existing state-of-the-art methods.