QQ客服 官方微博 反馈留言

浏览/检索结果: 共6条，第1-6条 帮助 已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 On the Scarcity of Dense Cores (n > 105 cm-3) in High-latitude Planck Galactic Cold Clumps 期刊论文 ASTROPHYSICAL JOURNAL LETTERS, 2024, 卷号: 963, 期号: 1 作者:  Xu, Fengwei;  Wang, Ke;  Liu, Tie;  Eden, David;  Liu, Xunchuan 收藏  |  浏览/下载：3/0  |  提交时间：2024/04/01 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 作者:  Wang, Li;  Yan, Zhenya 收藏  |  浏览/下载：2/0  |  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 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：5/0  |  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 Communications in Theoretical Physics, 2021, 卷号: 73, 期号: 10 作者:  Zhou,Zijian;  Yan,Zhenya 收藏  |  浏览/下载：4/0  |  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 PHYSICS LETTERS A, 2021, 卷号: 404, 页码: 7 作者:  Wang, Li;  Yan, Zhenya 收藏  |  浏览/下载：23/0  |  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   From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling 期刊论文 ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2019, 卷号: 8, 期号: 9, 页码: 16 作者:  Hou, Zhi-Wei;  Qin, Cheng-Zhi;  Zhu, A-Xing;  Liang, Peng;  Wang, Yi-Jie 收藏  |  浏览/下载：5/0  |  提交时间：2020/05/19 geographic modeling  input data preparation  intelligent geoprocessing  service composition