Electromagnetic scattering modeling combined with machine learning

This paper presents an efficient electromagnetic scattering modeling method that integrates machine learning with physical prior knowledge. The proposed algorithm aims to overcome the limitations of existing approaches by synergistically combining data-driven, physics-driven, and intelligent optimization strategies, thereby enhancing modeling efficiency, accuracy, and generalization capability. The core contributions of this study include: introducing few-shot learning and incremental update mechanisms to significantly reduce the dependence on large-scale training datasets and enable rapid online fine-tuning; designing a lightweight hybrid network architecture to improve inference speed and reduce hardware deployment costs while maintaining high accuracy; explicitly embedding electromagnetic physical constraints through the construction of a Physics-Informed Neural Network (PINN)[1] framework and a multi-task loss function, ensuring the physical consistency and interpretability of model predictions; and incorporating Generative Adversarial Networks (GANs)[2] for auxiliary data generation alongside active learning strategies to mitigate reliance on costly traditional simulations and enhance model robustness in practical scenarios. The proposed method is expected to substantially reduce the computational complexity of electromagnetic scattering modeling, improve model reliability and interpretability, and strengthen adaptability in complex and dynamic electromagnetic environments, thereby providing strong technical support for real-time applications such as radar target recognition and stealth design.