Physics-Informed Deep Contrast Source Inversion: A Unified Framework for Inverse Scattering Problems

TL;DR

This paper introduces DeepCSI, a physics-informed neural network framework that significantly improves the speed and accuracy of solving inverse scattering problems across various measurement scenarios by linearizing the nonlinear problem and jointly optimizing medium parameters.

Contribution

The paper presents a novel deep learning framework that unifies and enhances inverse scattering solutions using physics-informed neural networks and contrast source inversion techniques.

Findings

Achieves high-precision reconstructions in diverse measurement conditions

Reduces computational cost compared to traditional full-waveform inversion

Outperforms conventional CSI methods in robustness and accuracy

Abstract

Inverse scattering problems are critical in electromagnetic imaging and medical diagnostics but are challenged by their nonlinearity and diverse measurement scenarios. This paper proposes a physics-informed deep contrast source inversion framework (DeepCSI) for fast and accurate medium reconstruction across various measurement conditions. Inspired by contrast source inversion (CSI) and neural operator methods, a residual multilayer perceptron (ResMLP) is employed to model current distributions in the region of interest under different transmitter excitations, effectively linearizing the nonlinear inverse scattering problem and significantly reducing the computational cost of traditional full-waveform inversion. By modeling medium parameters as learnable tensors and utilizing a hybrid loss function that integrates state equation loss, data equation loss, and total variation regularization, DeepCSI establishes a fully differentiable framework for joint optimization of network parameters and medium properties. Compared with conventional methods, DeepCSI offers advantages in terms of simplicity and universal modeling capabilities for diverse measurement scenarios, including phase-less and multi-frequency observation. Simulations and experiments demonstrate that DeepCSI achieves high-precision, robust reconstruction under full-data, phaseless data, and multifrequency conditions, outperforming traditional CSI methods and providing an efficient and universal solution for complex inverse scattering problems.