Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems
This work addresses inverse scattering problems for applications like medical imaging or radar, with incremental improvements in method and efficiency.
The paper tackles electromagnetic inverse scattering problems by developing an improved physics-driven neural network with a new activation function and adaptive domain refinement, achieving superior reconstruction accuracy, robustness, and efficiency compared to state-of-the-art methods.
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.