# Linear-Model-inspired Neural Network for Electromagnetic Inverse Scattering

arXiv: https://arxiv.org/abs/2003.01465 · HTML: https://mlsanity.com/paper/2003.01465/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [A Model-Guided Neural Network Method for the Inverse Scattering Problem](https://mlsanity.com/paper/2512.10123/) (2025)

## Abstract (arXiv)

Electromagnetic inverse scattering problems (ISPs) aim to retrieve permittivities of dielectric scatterers from the scattering measurement. It is often highly nonlinear, caus-ing the problem to be very difficult to solve. To alleviate the issue, this letter exploits a linear model-based network (LMN) learning strategy, which benefits from both model complexity and data learning. By introducing a linear model for ISPs, a new model with network-driven regular-izer is proposed. For attaining efficient end-to-end learning, the network architecture and hyper-parameter estimation are presented. Experimental results validate its superiority to some state-of-the-arts.