# Multi-frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems

arXiv: https://arxiv.org/abs/2409.01315 · HTML: https://mlsanity.com/paper/2409.01315/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 82 references that are in the map.
- First posted on arXiv 2 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Natural Language Processing: State of The Art, Current Trends and Challenges](https://mlsanity.com/paper/1708.05148/) (2017)
- [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)
- [An Overview of Machine Learning Techniques for Radiowave Propagation Modeling](https://mlsanity.com/paper/2101.11760/) (2021)
- [3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion](https://mlsanity.com/paper/2305.05425/) (2023)
- [ANN-assisted CoSaMP Algorithm for Linear Electromagnetic Imaging of Spatially Sparse Domains](https://mlsanity.com/paper/1911.06514/) (2019)

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

- [A Differentiable Framework for Full and Phaseless Data Inversion Using Neural Implicit Contrast-Source Representation](https://mlsanity.com/paper/2508.10555/) (2025)
- [Multi-Dataset Inverse Problem Solving with Distributed Generative AI](https://mlsanity.com/paper/2608.26283/) (2026)

## Abstract (arXiv)

In this work, we propose a deep learning-based imaging method for addressing the multi-frequency electromagnetic (EM) inverse scattering problem (ISP). By combining deep learning technology with EM physical laws, we have successfully developed a multi-frequency neural Born iterative method (NeuralBIM), guided by the principles of the single-frequency NeuralBIM. This method integrates multitask learning techniques with NeuralBIM's efficient iterative inversion process to construct a robust multi-frequency Born iterative inversion model. During training, the model employs a multitask learning approach guided by homoscedastic uncertainty to adaptively allocate the weights of each frequency's data. Additionally, an unsupervised learning method, constrained by the physical laws of ISP, is used to train the multi-frequency NeuralBIM model, eliminating the need for contrast and total field data. The effectiveness of the multi-frequency NeuralBIM is validated through synthetic and experimental data, demonstrating improvements in accuracy and computational efficiency for solving ISP. Moreover, this method exhibits strong generalization capabilities and noise resistance. The multi-frequency NeuralBIM method explores a novel inversion method for multi-frequency EM data and provides an effective solution for the electromagnetic ISP of multi-frequency data.