Data-driven rogue waves and parameters discovery in nearly integrable PT-symmetric Gross-Pitaevskii equations via PINNs deep learning

doi:10.1016/j.physd.2022.133430

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	Data-driven rogue waves and parameters discovery in nearly integrable PT-symmetric Gross-Pitaevskii equations via PINNs deep learning
	Zhong, Ming 1,2; Gong, Shibo 3; Tian, Shou-Fu 4; Yan, Zhenya 1,2,3
	2022-11-01
发表期刊	PHYSICA D-NONLINEAR PHENOMENA
ISSN	0167-2789
卷号	439 页码:12
摘要	In this paper, we explore the forward and inverse problems for the generalized Gross-Pitaevskii (GP) equation with complex PT-symmetric potentials via the deep physics-informed neural networks (PINNs). The data-driven rogue waves (RWs) are mainly studied in the forward problem, where the comparisons between the data-driven RWs and numerical ones via the spectral method are used to present the PINNs solution accuracies. Besides, we also focus on the influences of several critical factors (e.g., the depths of neural networks, numbers of training points) on the performance of the PINNs algorithm. Finally, the inverse problem is also investigated such that the system parameters can be identified from the training data. The results obtained in this paper can be useful to further understand the neural networks on rogue wave structures in the nearly integrable PT-symmetric nonlinear wave systems. (C) 2022 Published by Elsevier B.V.
关键词	GeneralizedGrossPitaevskiiequation ComplexPT-symmetricpotentials Physics-informeddeepneuralnetworks Data-driven rogue waves and parameters discovery discovery
DOI	10.1016/j.physd.2022.133430
收录类别	SCI
语种	英语
资助项目	National Natural Sci- ence Foundation of China[11925108] ; National Natural Sci- ence Foundation of China[11731014] ; National Natural Science Foundation of China[11975306] ; Natural Science Foundation of Jiangsu Province of China[BK20181351] ; Six Talent Peaks Project of Jiangsu Province of China[JY-059]
WOS研究方向	Mathematics ; Physics
WOS类目	Mathematics, Applied ; Physics, Fluids & Plasmas ; Physics, Multidisciplinary ; Physics, Mathematical
WOS记录号	WOS:000830901400003
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/61161
专题	中国科学院数学与系统科学研究院
通讯作者	Yan, Zhenya
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, KLMM, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 3.Chongqing Univ, Sch Big Data & Software Engn, Chongqing 400044, Peoples R China 4.China Univ Min & Technol, Sch Math, Xuzhou 221116, Peoples R China
推荐引用方式 GB/T 7714	Zhong, Ming,Gong, Shibo,Tian, Shou-Fu,et al. Data-driven rogue waves and parameters discovery in nearly integrable PT-symmetric Gross-Pitaevskii equations via PINNs deep learning[J]. PHYSICA D-NONLINEAR PHENOMENA,2022,439:12.
APA	Zhong, Ming,Gong, Shibo,Tian, Shou-Fu,&Yan, Zhenya.(2022).Data-driven rogue waves and parameters discovery in nearly integrable PT-symmetric Gross-Pitaevskii equations via PINNs deep learning.PHYSICA D-NONLINEAR PHENOMENA,439,12.
MLA	Zhong, Ming,et al."Data-driven rogue waves and parameters discovery in nearly integrable PT-symmetric Gross-Pitaevskii equations via PINNs deep learning".PHYSICA D-NONLINEAR PHENOMENA 439(2022):12.