| 题名 | Search for rogue waves in Bose-Einstein condensates via a theory-guided neural network |
| 作者 | |
| 通讯作者 | Zhang,Dongxiao |
| 发表日期 | 2022-08-01
|
| DOI | |
| 发表期刊 | |
| ISSN | 2470-0045
|
| EISSN | 2470-0053
|
| 卷号 | 106期号:2 |
| 摘要 | An important and incompletely answered question is whether machine learning methods can be used to discover the excitation of rogue waves (RWs) in nonlinear systems, especially their dynamic properties and phase transitions. In this work, a theory-guided neural network (TgNN) is constructed to explore the RWs of one-dimensional Bose-Einstein condensates. We find that such method is superior to the ordinary deep neural network due to theory guidance of underlying problems. The former can directly give any excited location, timing, and structure of RWs using only a small amount of dynamic evolution data as the training data, without the tedious step-by-step iterative calculation process. In addition, based on the TgNN approach, a phase transition boundary is also discovered, which clearly distinguishes the first-order RW phase from the non-RW phase. The results not only greatly reduce computational time for exploring RWs, but also provide a promising technique for discovering phase transitions in parameterized nonlinear systems. |
| 相关链接 | [Scopus记录] |
| 收录类别 | |
| 语种 | 英语
|
| 学校署名 | 通讯
|
| WOS研究方向 | Physics
|
| WOS类目 | Physics, Fluids & Plasmas
; Physics, Mathematical
|
| WOS记录号 | WOS:000862890200008
|
| 出版者 | |
| EI入藏号 | 20223412609271
|
| EI主题词 | Bose-Einstein condensation
; Computation theory
; Deep neural networks
; Iterative methods
; Learning systems
; One dimensional
; Statistical mechanics
|
| EI分类号 | Ergonomics and Human Factors Engineering:461.4
; Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1
; Numerical Methods:921.6
; Mathematical Statistics:922.2
; Mechanics:931.1
; Physical Properties of Gases, Liquids and Solids:931.2
; Atomic and Molecular Physics:931.3
; Systems Science:961
|
| ESI学科分类 | PHYSICS
|
| Scopus记录号 | 2-s2.0-85136104670
|
| 来源库 | Scopus
|
| 引用统计 |
被引频次[WOS]:3
|
| 成果类型 | 期刊论文 |
| 条目标识符 | http://kc.sustech.edu.cn/handle/2SGJ60CL/401631 |
| 专题 | 深圳国家应用数学中心 |
| 作者单位 | 1.Department of Mathematics and Theories,Peng Cheng Laboratory,Shenzhen,Guangdong,518055,China 2.National Center for Applied Mathematics Shenzhen (NCAMS),Southern University of Science and Technology,Shenzhen,Guangdong,518055,China |
| 通讯作者单位 | 深圳国家应用数学中心 |
| 推荐引用方式 GB/T 7714 |
Bai,Xiao Dong,Zhang,Dongxiao. Search for rogue waves in Bose-Einstein condensates via a theory-guided neural network[J]. Physical Review E,2022,106(2).
|
| APA |
Bai,Xiao Dong,&Zhang,Dongxiao.(2022).Search for rogue waves in Bose-Einstein condensates via a theory-guided neural network.Physical Review E,106(2).
|
| MLA |
Bai,Xiao Dong,et al."Search for rogue waves in Bose-Einstein condensates via a theory-guided neural network".Physical Review E 106.2(2022).
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| 条目包含的文件 | 条目无相关文件。 | |||||
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