机构

• 数学与系统科学研究院 [5]
• 力学研究所 [3]
• 烟台海岸带研究所 [2]
• 海洋研究所 [1]

采集方式

• OAI收割 [11]

内容类型

• 期刊论文 [10]
• CNKI期刊论文 [1]

发表日期

• 2025 [1]
• 2024 [1]
• 2023 [2]
• 2022 [4]
• 2021 [3]

学科主题

筛选

浏览/检索结果: 共11条，第1-10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   AsPINN: Adaptive symmetry-recomposition physics-informed neural networks 期刊论文  OAI收割 COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2024, 卷号: 432, 页码: 34 作者:  Liu ZT(刘子提);  Liu Y(刘洋);  Yan, Xunshi;  Liu W(刘文);  Guo SQ(郭帅旗)   |  收藏  |  浏览/下载：8/0  |  提交时间：2024/11/01 Network structure  Parameter-sharing  Feature-enhanced physics-informed neural  networks  Symmetry decomposition   A Review of Application of Machine Learning in Storm Surge Problems 期刊论文  OAI收割 JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2023, 卷号: 11, 期号: 9, 页码: 35 作者:  Qin, Yue;  Su, Changyu;  Chu, Dongdong;  Zhang, Jicai;  Song, Jinbao   |  收藏  |  浏览/下载：21/0  |  提交时间：2023/11/15 storm surge prediction  machine learning  hybrid methods  physics-informed neural networks   A Review of Application of Machine Learning in Storm Surge Problems 期刊论文  OAI收割 JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2023, 卷号: 11, 期号: 9, 页码: 35 作者:  Qin, Yue;  Su, Changyu;  Chu, Dongdong;  Zhang, Jicai;  Song, Jinbao   |  收藏  |  浏览/下载：10/0  |  提交时间：2024/11/02 storm surge prediction  machine learning  hybrid methods  physics-informed neural networks   Monte Carlo fPINNs: Deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations 期刊论文  OAI收割 COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2022, 卷号: 400, 页码: 17 作者:  Guo, Ling;  Wu, Hao;  Yu, Xiaochen;  Zhou, Tao   |  收藏  |  浏览/下载：47/0  |  提交时间：2023/02/07 Physics -informed neural networks  Fractional Laplacian  Nonlocal operators  Uncertainty quantification   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：1/0  |  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 Deep Learning for The Multi-Hump Solitons and Parameters Discovery in NLS Equations with Generalized PT-Scarf-II Potentials 期刊论文  OAI收割 NEURAL PROCESSING LETTERS, 2022, 页码: 19 作者:  Zhong, Ming;  Zhang, Jian-Guo;  Zhou, Zijian;  Tian, Shou-Fu;  Yan, Zhenya   |  收藏  |  浏览/下载：16/0  |  提交时间：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   A Direct-Forcing Immersed Boundary Method for Incompressible Flows Based on Physics-Informed Neural Network 期刊论文  OAI收割 Fluids, 2022, 卷号: 7, 期号: 2, 页码: 56 作者:  Huang Y(黄毅);  Zhang ZY(张治愚);  Zhang X(张星)   |  收藏  |  浏览/下载：35/0  |  提交时间：2022/01/27 physics-informed neural networks (PINN)  direct-forcing immersed boundary method  incompressible laminar flow  circular cylinder   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：32/0  |  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：24/0  |  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