基于深度学习的超材料设计及光纤光束控制研究进展  

Research Progress in Metamaterial Design and Fiber Beam Control Based on Deep Learning

作　　者：罗仪豪 张峻[2] 杜世银 颜求泉 赵泽宇 陶梓隆 周侗 江天[3] Luo Yihao;Zhang Jun;Du Shiyin;Yan Qiuquan;Zhao Zeyu;Tao Zilong;Zhou Tong;Jiang Tian(College of Advanced Interdisciplinary Studies,National University of Defense Technology,Changsha 410073,Hunan,China;Institute for Quantum Information and the State Key Laboratory of High Performance Computing,College of Computer Science and Technology,National University of Defense Technology,Changsha 410073,Hunan,China;Institute of Quantum Information Science and Technology,College of Science,National University of Defense Technology,Changsha 410073,Hunan,China)

机构地区：[1]国防科技大学前沿交叉学科学院,湖南长沙410073 [2]国防科技大学计算机学院量子信息研究所兼高性能计算国家重点实验室,湖南长沙410073 [3]国防科技大学理学院量子信息研究所,湖南长沙410073

出　　处：《中国激光》2023年第11期96-114,共19页Chinese Journal of Lasers

基　　金：国家重点研发计划(2020YFB2205804,2020YFB2205800)。

摘　　要：超材料设计和光纤光束控制是光场调控研究的两个重要议题。传统方法取得一定研究进展的同时,也面临着有效性和适应性的问题。为弥补传统方法的不足,研究者们尝试将深度学习方法应用于以上两个议题。介绍了基于深度学习进行超材料设计和光纤光束控制的近期研究工作。超材料设计方面,重点回顾了采用多层感知机、卷积神经网络、循环神经网络、生成对抗网络等经典神经网络模型的相关工作;光纤光束控制方面,主要介绍了典型的搜索方法与深度强化学习方法。基于深度学习进行超材料设计和光纤光束控制的方法,具有速度快和自动化程度高的优势,为光场调控集成化、智能化提供新思路。Significance Metamaterial design and fiber beam control are two important topics in the study of optical field manipulation.Metamaterials are artificial materials with periodic structures and physical properties that do not exist naturally in the world.Suitable structural designs are crucial for achieving the potential of metamaterials.Numerical calculations and parameter optimization methods such as finite difference time domain(FDTD),finite element method(FEM),rigorous coupled wave analysis(RCWA),and genetic algorithms are commonly used in metamaterials design.However,these methods suffer from high computational costs and strong dependence on expert experience.Specifically,the high computational cost is due to the complexity of solving partial differential equations,while the reliance on expert experience stems from the fact that these numerical calculation methods depend on physical modeling.Additionally,parameter optimization algorithms also suffer from high computational costs due to the explosion of parameter combinations and repeated calls to numerical computation methods.Therefore,many researchers have turned to deep learning methods,attempting to use a data-driven approach to allow neural network models to learn the mapping relationship between metamaterial structure and optical response during the feature learning process,thus achieving accurate and efficient metamaterial design while shielding underlying physical details.Fiber beam control refers to adjusting parameters such as amplitude,phase,and polarization of a fiber optic beam to obtain novel features or stable states.Traditional methods mainly include genetic algorithms,stochastic parallel gradient descent(SPGD)algorithm,PID,and other search methods,which are limited by their inability to effectively solve system control problems in complex environments,i.e.,speed and accuracy issues.These optimization methods have simple strategies that are unable to generate good behavioral paths,resulting in too many steps to reach the target state.Moreover,th

关 键 词：材料 光纤光学 神经网络 光场调控 超材料设计 光学系统控制 

分 类 号：O439[机械工程—光学工程]