新激活函数+自适应区域划分，提升电磁反散射成像精度与效率
Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems
- 引入GLOW激活函数抑制振荡，稳定训练过程
- 动态识别散射区域，降低计算量并避免漏检
- 融合迁移学习，兼顾物理可解释性与实时推理
本文提出一种改进的物理驱动神经网络（IPDNN）框架，用于求解电磁逆散射问题。引入新型高斯局部化振荡抑制窗（GLOW）激活函数，提升收敛稳定性，实现轻量化且高精度的网络结构。进一步设计动态散射子区域识别策略，自适应优化计算域，防止遗漏检测并降低计算成本。此外，通过迁移学习增强求解器在实际场景中的适用性，结合迭代算法的物理可解释性与神经网络的实时推理能力。数值仿真与实验结果表明，该方法在重建精度、鲁棒性与效率方面均优于现有先进方法。
原文摘要 · Abstract (English)
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.
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