机构

• 数学与系统科学研究院 [4]
• 力学研究所 [2]
• 海洋研究所 [1]

采集方式

• OAI收割 [7]

内容类型

• 期刊论文 [6]
• CNKI期刊论文 [1]

发表日期

• 2025 [1]
• 2024 [1]
• 2022 [1]
• 2021 [3]
• 2020 [1]

学科主题

筛选

浏览/检索结果: 共7条，第1-7条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Direct numerical simulation of natural convection based on parameter-input physics-informed neural networks 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2025, 卷号: 236, 页码: 126379 作者:  Ye,Shuran;  Huang JL(黄剑霖);  Zhang, Zhen;  Wang YW(王一伟);  Huang CG(黄晨光)   |  收藏  |  浏览/下载：3/0  |  提交时间：2024/12/02 Natural convection  Physics-informed neural networks  Parameter-input PINNs  Ra number  Deep learning   A framework of data assimilation for wind flow fields by physics-informed neural networks 期刊论文  OAI收割 APPLIED ENERGY, 2024, 卷号: 371, 页码: 18 作者:  Yan C(闫畅);  Xu SF(许盛峰);  Sun ZX(孙振旭);  Lutz, Thorsten;  Guo DL(郭迪龙)   |  收藏  |  浏览/下载：3/0  |  提交时间：2024/11/22 Data assimilation  Wind field reconstruction  Physics-informed deep learning   Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations CNKI期刊论文  OAI收割 2022 作者:  Yuchao Zhu;  Rong-Hua Zhang;  James N.Moum;  Fan Wang;  Xiaofeng Li   |  收藏  |  浏览/下载：2/0  |  提交时间：2024/12/18 physics-informed deep learning  climate model biases  ocean vertical-mixing parameterizations  long-term turbulence data  artificial neural networks under physics constraint   Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning 期刊论文  OAI收割 PHYSICA D-NONLINEAR PHENOMENA, 2021, 卷号: 428, 页码: 15 作者:  Wang, Li;  Yan, Zhenya   |  收藏  |  浏览/下载：34/0  |  提交时间：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   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  OAI收割 PHYSICS LETTERS A, 2021, 卷号: 404, 页码: 7 作者:  Wang, Li;  Yan, Zhenya   |  收藏  |  浏览/下载：25/0  |  提交时间：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  OAI收割 PHYSICS LETTERS A, 2021, 卷号: 387, 页码: 12 作者:  Zhou, Zijian;  Yan, Zhenya   |  收藏  |  浏览/下载：27/0  |  提交时间：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  OAI收割 NEURAL NETWORKS, 2020, 卷号: 132, 页码: 166-179 作者:  Jin, Pengzhan;  Zhang, Zhen;  Zhu, Aiqing;  Tang, Yifa;  Karniadakis, George Em   |  收藏  |  浏览/下载：27/0  |  提交时间：2021/04/26 Deep learning  Physics-informed  Dynamical systems  Hamiltonian systems  Symplectic maps  Symplectic integrators