深度学习策略下光纤中超短脉冲非线性传输过程表征及控制研究进展  

Nonlinear Propagation Representation and Control for Ultrashort Pulse in Optical Fibers Based on Deep Learning

作　　者：隋皓 朱宏娜[1] 贾焕玉[1] 欧洺余 李祺 罗斌[2] 邹喜华[2] Sui Hao;Zhu Hongna;Jia Huanyu;Ou Mingyu;Li Qi;Luo Bin;Zou Xihua(School of Physical Science and Technology,Southwest Jiaotong University,Chengdu 610031,Sichuan,China;School of Information Science and Technology,Southwest Jiaotong University,Chengdu 610031,Sichuan,China)

机构地区：[1]西南交通大学物理科学与技术学院,成都四川610031 [2]西南交通大学信息科学与技术学院,成都四川610031

出　　处：《中国激光》2023年第11期162-170,共9页Chinese Journal of Lasers

基　　金：国家重点研发计划项目(2019YFB1803500);四川省科技计划项目(2020YJ0016)。

摘　　要：常规数值求解方法在表征光纤中超短脉冲的非线性传输过程时存在计算量大、效率低等局限。随着人工智能的快速发展,深度学习技术展现出了强大的计算能力、广泛的适用范围、良好的硬件移植性,在光纤中超短脉冲非线性传输过程表征和控制研究中具有巨大潜力。本文概述了深度学习技术及其在预测光纤中超短脉冲传输、超短脉冲重构及参数估计方面的研究进展,同时展望了深度学习与光纤中超短脉冲非线性传输这一新兴交叉技术的发展方向和挑战。Significance Nonlinear ultrashort laser pulse propagation in optical fibers,which is the physical principle of fiber-based optical devices,optical signal transmission,and processing,comprises a series of complex nonlinear dynamics.It finds extensive application in the fields of fiber lasers,fiber amplifiers,and fiber communications.Generally,nonlinear ultrashort pulse propagation is governed by the nonlinear Schrödinger equation(NLSE)and can be solved using model-driven methods such as the split-step Fourier(SSF)and finite-difference methods.However,NLSE-based systems are sensitive to both the initial pulse and fiber parameters,making it difficult for traditional numerical methods to control the complex nonlinear pulse evolution in a time-efficient manner.As a powerful tool for system parameter optimization and the construction of models of complex dynamics from observed data,deep learning(DL)algorithms have recently been applied to ultrafast photonics,optical communications,optical networks,optical imaging,and the modeling and control of nonlinear pulse propagation to reap the benefits of purely data-driven methods without any underlying governing equations.In this paper,the current key technologies and applications of the DL method for predicting nonlinear pulse dynamics in fiber optics,reconstructing ultrashort pulses,and evaluating critical pulse characteristics are summarized,and the development trends are predicted.Progress First,a brief introduction to the DL method and practical DNN is presented.Second,the applications of DL for predicting nonlinear ultrashort pulse propagation are listed.Several types of neural networks,i.e.,LSTM,CNN,and FNN,have been applied to predict nonlinear pulse evolution,i.e.,predicting the effects of GVD and SPM on ultrashort pulse propagation,higher-order soliton compression,and supercontinuum generation,in both the temporal and spectral domains with high prediction precision.Moreover,DL methods are used for modeling optical fiber channels,resulting in a significant reduction

关 键 词：光纤光学 超短激光脉冲传输 非线性薛定谔方程 光纤非线性效应 深度学习 

分 类 号：O437[机械工程—光学工程]