深度学习
卷积神经网络
计算机科学
编码(社会科学)
趋同(经济学)
均方误差
反向
极限(数学)
算法
计算机工程
人工智能
人工神经网络
统计
数学
数学分析
几何学
经济
经济增长
作者
Jiahui Fu,yuping zhang,zhongxin dou,Zhihu Yang,Meng Liu,huiyun zhang
出处
期刊:Applied Optics
[The Optical Society]
日期:2023-04-26
卷期号:62 (13): 3502-3502
被引量:1
摘要
This paper proposes a deep-learning-assisted design method for 2-bit coding metasurfaces. This method uses a skip connection module and the idea of an attention mechanism in squeeze-and-excitation networks based on a fully connected network and a convolutional neural network. The accuracy limit of the basic model is further improved. The convergence ability of the model increased nearly 10 times, and the mean-square error loss function converges to 0.000168. The forward prediction accuracy of the deep-learning-assisted model is 98%, and the accuracy of inverse design results is 97%. This approach offers the advantages of an automatic design process, high efficiency, and low computational cost. It can serve users who lack metasurface design experience.
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