用物理模型+深度学习，高效预测频率选择表面电磁特性
Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning
- 结合电路模型与深度学习，从结构参数直接预测电磁响应
- 相位预测精度提升，模型更小、计算更快、泛化更强
- 适合需要快速仿真且数据稀缺的电磁设计场景
本文提出一种端到端的基于物理模型的深度学习方法，用于高效分析高维频率选择表面（FSS）的电磁特性。该方法融合等效电路模型的物理先验与深度学习技术，无需大量数据即可显著降低模型复杂度并提高预测精度。相比以往基于模型的学习方法，本方案从FSS几何结构直接端到端训练至其S参数响应。通过改进损失函数，进一步提升了相位预测精度。与直接建模方法（如DNN、RBFN）相比，该方法在计算效率、模型规模和泛化能力上均表现更优。
原文摘要 · Abstract (English)
This paper introduces an innovative end-to-end model-based deep learning approach for efficient electromagnetic analysis of high-dimensional frequency selective surfaces (FSS). Unlike traditional data-driven methods that require large datasets, this approach combines physical insights from equivalent circuit models with deep learning techniques to significantly reduce model complexity and enhance prediction accuracy. Compared to previously introduced model-based learning approaches, the proposed method is trained end-to-end from the physical structure of the FSS (geometric parameters) to its electromagnetic response (S-parameters). Additionally, an improvement in phase prediction accuracy through a modified loss function is presented. Comparisons with direct models, including deep neural networks (DNN) and radial basis function networks (RBFN), demonstrate the superiority of the model-based approach in terms of computational efficiency, model size, and generalization capability.
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