Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems

TL;DR

This paper introduces an enhanced physics-driven neural network for electromagnetic inverse scattering, featuring a novel activation function, adaptive domain refinement, and transfer learning to improve accuracy, robustness, and efficiency.

Contribution

The paper proposes a new neural network framework with a specialized activation function, adaptive subregion identification, and transfer learning for better inverse scattering solutions.

Findings

Achieves superior reconstruction accuracy compared to existing methods.

Demonstrates robustness and efficiency in numerical and experimental tests.

Integrates physical interpretability with real-time neural inference.

Abstract

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.