# Physics Embedded Machine Learning for Electromagnetic Data Imaging

arXiv: https://arxiv.org/abs/2207.12607 · HTML: https://mlsanity.com/paper/2207.12607/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 75 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Compressed Sensing using Generative Models](https://mlsanity.com/paper/1703.03208/) (2017)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [DeepNIS: Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering](https://mlsanity.com/paper/1810.03990/) (2018)
- [Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems](https://mlsanity.com/paper/2103.15138/) (2021)
- [Deep Unrolled Recovery in Sparse Biological Imaging](https://mlsanity.com/paper/2109.14025/) (2021)
- [Neural Born Iteration Method For Solving Inverse Scattering Problems: 2D Cases](https://mlsanity.com/paper/2112.09831/) (2021)
- [Error Control and Loss Functions for the Deep Learning Inversion of Borehole Resistivity Measurements](https://mlsanity.com/paper/2005.08868/) (2020)
- [A Deep Learning Approach to the Inversion of Borehole Resistivity Measurements](https://mlsanity.com/paper/1810.04522/) (2018)
- [Structured LISTA for Multidimensional Harmonic Retrieval](https://mlsanity.com/paper/2102.11663/) (2021)
- [Graph Attention Network For Microwave Imaging of Brain Anomaly](https://mlsanity.com/paper/2108.01965/) (2021)
- [Block-Sparse Recovery Network for Two-Dimensional Harmonic Retrieval](https://mlsanity.com/paper/2111.07589/) (2021)

## Built on by (most cited first, 11 of 11)

- [Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey](https://mlsanity.com/paper/2411.05027/) (2024)
- [Quality-factor inspired deep neural network solver for solving inverse scattering problems](https://mlsanity.com/paper/2504.20504/) (2025)
- [A Comparative Study of Variational Autoencoders, Normalizing Flows, and Score-based Diffusion Models for Electrical Impedance Tomography](https://mlsanity.com/paper/2310.15831/) (2023)
- [A Two-Stage Imaging Framework Combining CNN and Physics-Informed Neural Networks for Full-Inverse Tomography: A Case Study in Electrical Impedance Tomography (EIT)](https://mlsanity.com/paper/2407.17721/) (2024)
- [Graph-CNNs for RF Imaging: Learning the Electric Field Integral Equations](https://mlsanity.com/paper/2503.14439/) (2025)
- [Enhancing Electrical Impedance Tomography reconstruction using Learned Half-Quadratic Splitting Networks with Anderson Acceleration](https://mlsanity.com/paper/2304.14491/) (2023)
- [Real-Time Model-Based Quantitative Ultrasound and Radar](https://mlsanity.com/paper/2402.10520/) (2024)
- [Plug-and-Play Latent Diffusion for Electromagnetic Inverse Scattering with Application to Brain Imaging](https://mlsanity.com/paper/2509.04860/) (2025)
- [MCDIP-ADMM: Overcoming Overfitting in DIP-based CT reconstruction](https://mlsanity.com/paper/2304.03895/) (2023)
- [A Physics-Embedded Dual-Learning Imaging Framework for Electrical Impedance Tomography](https://mlsanity.com/paper/2412.17827/) (2024)
- [Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction](https://mlsanity.com/paper/2603.02231/) (2026)

## Abstract (arXiv)

Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive. Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. However, the high performance of purely data-driven approaches relies on constructing a training set that is statistically consistent with practical scenarios, which is often not possible in EM imaging tasks. Consequently, generalizability becomes a major concern. On the other hand, physical principles underlie EM phenomena and provide baselines for current imaging techniques. To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics embedded ML methods for EM imaging have become the focus of a large body of recent work. This article surveys various schemes to incorporate physics in learning-based EM imaging. We first introduce background on EM imaging and basic formulations of the inverse problem. We then focus on three types of strategies combining physics and ML for linear and nonlinear imaging and discuss their advantages and limitations. Finally, we conclude with open challenges and possible ways forward in this fast-developing field. Our aim is to facilitate the study of intelligent EM imaging methods that will be efficient, interpretable and controllable.