超材料
电磁感应透明
太赫兹辐射
反向
反问题
物理
计算机科学
透明度(行为)
振幅
光学
数学
数学分析
几何学
计算机安全
作者
Wei Huang,Ziming Wei,Benying Tan,Shan Yin,Wentao Zhang
标识
DOI:10.1088/1361-6463/abd4a6
摘要
Abstract In this paper, we apply the deep learning network to the inverse engineering of electromagnetically induced transparency (EIT) in terahertz metamaterial. We take three specific points of the EIT spectrum with six inputs (each specific point has two physical values with frequency and amplitude) into the deep learning model to predict and inversely design the geometrical parameters of EIT metamaterials. We propose this algorithm for the general inverse design of EIT metamaterials, and we demonstrate that our method is functional by taking one example structure. Our deep learning model exhibits a mean square error of 0.0085 in the training set and 0.014 in the test set. We believe that this finding will open a new approach for designing geometrical parameters of EIT metamaterials, and it has great potential to enlarge the applications of the THz EIT metamaterial.
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