Data-driven parity-time-symmetric vector rogue wave solutions of multi-component nonlinear Schrödinger equation  

作　　者：Li-Jun Chang Yi-Fan Mo Li-Ming Ling De-Lu Zeng 常莉君;莫一凡;凌黎明;曾德炉(School of Mathematics,South China University of Technology,Guangzhou 510640,China)

机构地区：[1]School of Mathematics,South China University of Technology,Guangzhou 510640,China

出　　处：《Chinese Physics B》2022年第6期137-144,共8页中国物理B（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.11771151,61571005,and 61901160);the Science and Technology Program of Guangzhou(Grant No.201904010362);the Fundamental Research Program of Guangdong Province,China(Grant No.2020B1515310023)。

摘　　要：Rogue waves are a class of nonlinear waves with extreme amplitudes,which usually appear suddenly and disappear without any trace.Recently,the parity-time(PT)-symmetric vector rogue waves(RWs)of multi-component nonlinear Schrödinger equation(n-NLSE)are usually derived by the methods of integrable systems.In this paper,we utilize the multi-stage physics-informed neural networks(MS-PINNs)algorithm to derive the data-driven symmetric vector RWs solution of coupled NLS system in elliptic and X-shapes domains with nonzero boundary condition.The results of the experiment show that the multi-stage physics-informed neural networks are quite feasible and effective for multi-component nonlinear physical systems in the above domains and boundary conditions.

关 键 词：nonlinear Schrödinger equation vector rogue waves deep learning numerical simulations 

分 类 号：O411[理学—理论物理]