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Deep learning neural networks for the third-order nonlinear Schr?dinger equation: bright solitons, breathers, and rogue waves
Zhou,Zijian1,2; Yan,Zhenya1,2
2021-09-03
发表期刊Communications in Theoretical Physics
ISSN0253-6102
卷号73期号:10
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.
关键词third-order nonlinear Schr?dinger equation deep learning data-driven solitons data-driven parameter discovery
DOI10.1088/1572-9494/ac1cd9
语种英语
WOS记录号IOP:0253-6102-73-10-ac1cd9
出版者IOP Publishing
引用统计
文献类型期刊论文
条目标识符http://ir.amss.ac.cn/handle/2S8OKBNM/59127
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
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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).
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