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中国科学院数学与系统科学研究院机构知识库
KMS Of Academy of mathematics and systems sciences, CAS
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2022 [4]
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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
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View/Download:27/0
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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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  |  
Submit date:2023/02/07
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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Submit date:2023/02/07
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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View/Download:31/0
  |  
Submit date:2023/02/07
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
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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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  |  
Submit date:2022/04/02
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
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  |  
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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  |  
Submit date:2021/10/26
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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View/Download:107/0
  |  
Submit date:2021/04/26
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
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View/Download:118/0
  |  
Submit date:2021/04/26
Deep learning
Physics-informed
Dynamical systems
Hamiltonian systems
Symplectic maps
Symplectic integrators