KMS Of Academy of mathematics and systems sciences, CAS
| Data-driven rogue waves and parameters discovery in nearly integrable PT-symmetric Gross-Pitaevskii equations via PINNs deep learning | |
| Zhong, Ming1,2; Gong, Shibo3; Tian, Shou-Fu4; Yan, Zhenya1,2,3 | |
| 2022-11-01 | |
| Source Publication | PHYSICA D-NONLINEAR PHENOMENA
 |
| ISSN | 0167-2789 |
| Volume | 439Pages:12 |
| Abstract | 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. |
| Keyword | GeneralizedGrossPitaevskiiequation ComplexPT-symmetricpotentials Physics-informeddeepneuralnetworks Data-driven rogue waves and parameters discovery discovery |
| DOI | 10.1016/j.physd.2022.133430 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 Research Area | Mathematics ; Physics |
| WOS Subject | Mathematics, Applied ; Physics, Fluids & Plasmas ; Physics, Multidisciplinary ; Physics, Mathematical |
| WOS ID | WOS:000830901400003 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/61161 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Yan, Zhenya |
| Affiliation | 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 |
| Recommended Citation 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. |
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