用深度学习加速多层超表面吸波结构的电磁响应预测
A Surrogate Model for the Forward Design of Multi-layered Metasurface-based Radar Absorbing Structures
- 基于卷积神经网络与Huber损失函数构建代理模型
- 训练1000轮后余弦相似度达99.9%，均方误差仅0.001
- 实验与仿真验证显著降低计算时间，适合快速设计
基于超表面的雷达吸波结构（RAS）因具备频率选择性吸收特性、厚度小、重量轻等优势，在隐身技术、电磁屏蔽等领域备受青睐。然而，传统电磁设计与优化依赖全波仿真工具进行正向模拟，以预测候选元原子的电磁响应，该过程计算量大、耗时极长，且需探索广阔的設計空间。为克服这一挑战，本文提出一种代理模型，显著加速多层超表面基RAS电磁响应的预测。采用基于卷积神经网络（CNN）的架构，并结合Huber损失函数，用于估计RAS的反射特性。所提模型在1000次训练后达到99.9%的余弦相似度和0.001的均方误差。通过全波仿真与实验验证，该模型在保持高预测精度的同时，大幅减少计算时间，展现出显著效率优势。
原文摘要 · Abstract (English)
Metasurface-based radar absorbing structures (RAS) are highly preferred for applications like stealth technology, electromagnetic (EM) shielding, etc. due to their capability to achieve frequency selective absorption characteristics with minimal thickness and reduced weight penalty. However, the conventional approach for the EM design and optimization of these structures relies on forward simulations, using full wave simulation tools, to predict the electromagnetic (EM) response of candidate meta atoms. This process is computationally intensive, extremely time consuming and requires exploration of large design spaces. To overcome this challenge, we propose a surrogate model that significantly accelerates the prediction of EM responses of multi-layered metasurface-based RAS. A convolutional neural network (CNN) based architecture with Huber loss function has been employed to estimate the reflection characteristics of the RAS model. The proposed model achieved a cosine similarity of 99.9% and a mean square error of 0.001 within 1000 epochs of training. The efficiency of the model has been established via full wave simulations as well as experiment where it demonstrated significant reduction in computational time while maintaining high predictive accuracy.
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