机构

• 数学与系统科学研究... [10]
• 力学研究所 [1]
• 地质与地球物理研究所 [1]

采集方式

• OAI收割 [12]

内容类型

• 期刊论文 [12]

发表日期

• 2024 [1]
• 2023 [1]
• 2022 [4]
• 2021 [5]
• 2020 [1]

学科主题

筛选

浏览/检索结果: 共12条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序题名升序题名降序作者升序作者降序发表日期升序发表日期降序 Stochastic dynamics of aircraft ground taxiing via improved physics-informed neural networks 期刊论文  OAI收割 NONLINEAR DYNAMICS, 2024, 页码: 16 作者:  Zhang, Ying;  Jin, Zhengrong;  Wang L(王笼);  Zheng, Kaixin;  Jia, Wantao   |  收藏  |  浏览/下载：8/0  |  提交时间：2024/02/19 PINNs  Aircraft ground taxiing model  Fokker-Planck equations  Inverse problem   Ground-penetrating radar wavefield simulation via physics-informed neural network solver 期刊论文  OAI收割 GEOPHYSICS, 2023, 卷号: 88, 期号: 2, 页码: KS47-KS57 作者:  Zheng, Yikang;  Wang, Yibo   |  收藏  |  浏览/下载：3/0  |  提交时间：2023/12/29 Data-driven rogue waves and parameters discovery in nearly integrable PT-symmetric Gross-Pitaevskii equations via PINNs deep learning 期刊论文  OAI收割 PHYSICA D-NONLINEAR PHENOMENA, 2022, 卷号: 439, 页码: 12 作者:  Zhong, Ming;  Gong, Shibo;  Tian, Shou-Fu;  Yan, Zhenya   |  收藏  |  浏览/下载：15/0  |  提交时间：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  OAI收割 PHYSICS OF FLUIDS, 2022, 卷号: 34, 期号: 10, 页码: 21 作者:  Zhang, Rui;  Hu, Peiyan;  Meng, Qi;  Wang, Yue;  Zhu, Rongchan   |  收藏  |  浏览/下载：15/0  |  提交时间：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  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：35/0  |  提交时间：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  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：7/0  |  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   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：19/0  |  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   Deep learning neural networks for the third-order nonlinear Schrodinger equation: bright solitons, breathers, and rogue waves 期刊论文  OAI收割 COMMUNICATIONS IN THEORETICAL PHYSICS, 2021, 卷号: 73, 期号: 10, 页码: 9 作者:  Zhou, Zijian;  Yan, Zhenya   |  收藏  |  浏览/下载：15/0  |  提交时间：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  OAI收割 Communications in Theoretical Physics, 2021, 卷号: 73, 期号: 10 作者:  Zhou,Zijian;  Yan,Zhenya   |  收藏  |  浏览/下载：17/0  |  提交时间：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  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：14/0  |  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