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Data-driven rogue waves and parameters discovery in nearly integrable PT-symmetric Gross-Pitaevskii equations via PINNs deep learning 期刊论文
PHYSICA D-NONLINEAR PHENOMENA, 2022, 卷号: 439, 页码: 12
Authors:  Zhong, Ming;  Gong, Shibo;  Tian, Shou-Fu;  Yan, Zhenya
Favorite  |  View/Download：27/0  |  Submit date：2023/02/07
GeneralizedGrossPitaevskiiequation  ComplexPT-symmetricpotentials  Physics-informeddeepneuralnetworks  Data-driven rogue waves and parameters discovery discovery  
DRVN (deep random vortex network): A new physics-informed machine learning method for simulating and inferring incompressible fluid flows 期刊论文
PHYSICS OF FLUIDS, 2022, 卷号: 34, 期号: 10, 页码: 21
Authors:  Zhang, Rui;  Hu, Peiyan;  Meng, Qi;  Wang, Yue;  Zhu, Rongchan;  Chen, Bingguang;  Ma, Zhi-Ming;  Liu, Tie-Yan
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Monte Carlo fPINNs: Deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations 期刊论文
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2022, 卷号: 400, 页码: 17
Authors:  Guo, Ling;  Wu, Hao;  Yu, Xiaochen;  Zhou, Tao
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Physics -informed neural networks  Fractional Laplacian  Nonlocal operators  Uncertainty quantification  
Data-Driven Deep Learning for The Multi-Hump Solitons and Parameters Discovery in NLS Equations with Generalized PT-Scarf-II Potentials 期刊论文
NEURAL PROCESSING LETTERS, 2022, 页码: 19
Authors:  Zhong, Ming;  Zhang, Jian-Guo;  Zhou, Zijian;  Tian, Shou-Fu;  Yan, Zhenya
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Focusing and defocusing nonlinear Schrodinger equations  Generalized PT-Scarf-II potential  Physics-informed deep neural networks  Data-driven solitons and parameters discovery  
Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning 期刊论文
PHYSICA D-NONLINEAR PHENOMENA, 2021, 卷号: 428, 页码: 15
Authors:  Wang, Li;  Yan, Zhenya
Favorite  |  View/Download：78/0  |  Submit date：2022/04/02
Nonlinear dispersive equation  Initial-boundary value conditions  Physics-informed neural networks  Deep learning  Data-driven peakon and periodic peakon  solutions Data-driven parameter discovery  
Deep learning neural networks for the third-order nonlinear Schrodinger equation: bright solitons, breathers, and rogue waves 期刊论文
COMMUNICATIONS IN THEORETICAL PHYSICS, 2021, 卷号: 73, 期号: 10, 页码: 9
Authors:  Zhou, Zijian;  Yan, Zhenya
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third-order nonlinear Schrodinger equation  deep learning  data-driven solitons  data-driven parameter discovery  
Deep learning neural networks for the third-order nonlinear Schr?dinger equation: bright solitons, breathers, and rogue waves 期刊论文
Communications in Theoretical Physics, 2021, 卷号: 73, 期号: 10
Authors:  Zhou,Zijian;  Yan,Zhenya
Favorite  |  View/Download：76/0  |  Submit date：2022/04/02
third-order nonlinear Schr?dinger equation  deep learning  data-driven solitons  data-driven parameter discovery  
Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrodinger equation with a potential using the PINN deep learning 期刊论文
PHYSICS LETTERS A, 2021, 卷号: 404, 页码: 7
Authors:  Wang, Li;  Yan, Zhenya
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Defocusing NLS equation with the  time-dependent potential  Initial-boundary value conditions  Physics-informed neural networks  Deep learning  Data-driven rogue waves and parameter discovery  
Solving forward and inverse problems of the logarithmic nonlinear Schrodinger equation with PT-symmetric harmonic potential via deep learning 期刊论文
PHYSICS LETTERS A, 2021, 卷号: 387, 页码: 12
Authors:  Zhou, Zijian;  Yan, Zhenya
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Logarithmic nonlinear Schrodinger equation  PT-symmetric potentials  Physics-informed neural networks  Deep learning  Data-driven discovery of LNLS equation  Data-driven solitons  
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems 期刊论文
NEURAL NETWORKS, 2020, 卷号: 132, 页码: 166-179
Authors:  Jin, Pengzhan;  Zhang, Zhen;  Zhu, Aiqing;  Tang, Yifa;  Karniadakis, George Em
Favorite  |  View/Download：118/0  |  Submit date：2021/04/26
Deep learning  Physics-informed  Dynamical systems  Hamiltonian systems  Symplectic maps  Symplectic integrators