计算机科学
深度学习
人工神经网络
学习迁移
遗传算法
领域(数学)
方案(数学)
电磁学
人工智能
电介质
算法
计算机工程
机器学习
电子工程
材料科学
光电子学
数学
工程类
数学分析
纯数学
作者
Xu Dong,Yu Luo,Jun Luo,Mingbo Pu,Yaxin Zhang,Yinli Ha,Xiangang Luo
摘要
Machine learning has been widely adopted in various disciplines as they offer low-computational cost solutions to complex problems. Recently, deep learning-enabled methods for metasurface design have received increasing attention in the field of subwavelength electromagnetics. However, the previous metasurface design methods based on deep learning usually use huge datasets or complex networks to make deep neural networks achieve high prediction accuracy which results in more time for dataset establishment and network training. Here, we propose an expeditious and accurate scheme for designing phase-modulating dielectric metasurface through employing the transfer learning technology and genetic algorithm. The performance of the neural network is improved distinctly by migrating knowledge between real part and imaginary part spectrum-prediction tasks. Furthermore, the target meta-atoms can be optimized readily without increasing a large dataset through transfer learning. Finally, we design two deflectors and two metalenses as a proof-of-concept demonstration to validate the ability of our proposed approach. The scheme provides an efficient and promising design method for phase-modulating metasurface.
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