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Deep learning neural networks for the third-order nonlinear Schr?dinger equation: bright solitons, breathers, and rogue waves

文献类型：期刊论文

作者Zhou,Zijian1,2; Yan,Zhenya1,2
刊名Communications in Theoretical Physics
出版日期2021-09-03
卷号73期号:10
关键词third-order nonlinear Schr?dinger equation deep learning data-driven solitons data-driven parameter discovery
ISSN号0253-6102
DOI10.1088/1572-9494/ac1cd9
英文摘要Abstract The dimensionless third-order nonlinear Schr?dinger 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.
语种英语
WOS记录号IOP:0253-6102-73-10-AC1CD9
出版者IOP Publishing
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/59126]  
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.Key Laboratory of Mathematics Mechanization, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
2.School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
推荐引用方式
GB/T 7714		 Zhou,Zijian,Yan,Zhenya. Deep learning neural networks for the third-order nonlinear Schr?dinger equation: bright solitons, breathers, and rogue waves[J]. Communications in Theoretical Physics,2021,73(10).
APA Zhou,Zijian,&Yan,Zhenya.(2021).Deep learning neural networks for the third-order nonlinear Schr?dinger equation: bright solitons, breathers, and rogue waves.Communications in Theoretical Physics,73(10).
MLA Zhou,Zijian,et al."Deep learning neural networks for the third-order nonlinear Schr?dinger equation: bright solitons, breathers, and rogue waves".Communications in Theoretical Physics 73.10(2021).

入库方式： OAI收割

来源：数学与系统科学研究院

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