Deep learning neural networks for the third-order nonlinear Schrodinger equation: bright solitons, breathers, and rogue waves
Zhou, Zijian1,2; Yan, Zhenya1,2
刊名COMMUNICATIONS IN THEORETICAL PHYSICS
2021-10-01
卷号73期号:10页码:9
关键词third-order nonlinear Schrodinger equation deep learning data-driven solitons data-driven parameter discovery
ISSN号0253-6102
DOI10.1088/1572-9494/ac1cd9
英文摘要The dimensionless third-order nonlinear Schrodinger equation (alias the Hirota equation) is investigated via deep leaning neural networks. In this paper, we use the physics-informed neural networks (PINNs) deep learning method to explore the data-driven solutions (e.g. bright soliton, breather, and rogue waves) of the Hirota equation when the two types of the unperturbated and perturbated (a 2% noise) training data are considered. Moreover, we use the PINNs deep learning to study the data-driven discovery of parameters appearing in the Hirota equation with the aid of bright solitons.
资助项目National Natural Science Foundation of China[11 925 108] ; National Natural Science Foundation of China[11 731 014]
WOS研究方向Physics
语种英语
出版者IOP PUBLISHING LTD
WOS记录号WOS:000692832500001
内容类型期刊论文
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/59192]  
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.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Math Mechanizat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Zhou, Zijian,Yan, Zhenya. Deep learning neural networks for the third-order nonlinear Schrodinger equation: bright solitons, breathers, and rogue waves[J]. COMMUNICATIONS IN THEORETICAL PHYSICS,2021,73(10):9.
APA Zhou, Zijian,&Yan, Zhenya.(2021).Deep learning neural networks for the third-order nonlinear Schrodinger equation: bright solitons, breathers, and rogue waves.COMMUNICATIONS IN THEORETICAL PHYSICS,73(10),9.
MLA Zhou, Zijian,et al."Deep learning neural networks for the third-order nonlinear Schrodinger equation: bright solitons, breathers, and rogue waves".COMMUNICATIONS IN THEORETICAL PHYSICS 73.10(2021):9.
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