.png){kind=icon}
返回
From structured data to evolution linear partial differential equations
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jcp.2019.04.049.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper is devoted to the derivation of computational methods for constructing partial differential equations from data. Following some recent works [7,14,15,20], we propose a methodology based on symbolic calculus [8,9,13], pseudospectral methods [2,3] and stochastic processes [6], in order to determine non-constant coefficients of linear evolution Partial Differential Equations (PDEs), from a set of structured data constituted by solutions at given times and positions, of an unknown linear PDE. Crown Copyright (C) 2019 Published by Elsevier Inc. All rights reserved.
Keyword:
Partial differential equations
Numerical approximation
Operator symbols
Pseudospectral methods
Inverse problems
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.8
论文数:
1.6W
被引数:
7.4W
机构
引用论文
Asymmetric transmembrane transfer caused by a difference in adsorption characteristics at interfaces
PREAI
分享收藏Discovering governing equations from data by sparse identification of nonlinear dynamical systems通过非线性动力系统的稀疏识别从数据中发现控制方程
OAAI
OAAI分享收藏
Hydrogen Mobility Europe (H2ME): Vehicle and Hydrogen Refuelling Station Deployment Results欧洲氢能移动 (H2ME): 车辆和加氢站部署结果OAAI
OAAI分享收藏分享收藏The sub‐zero temperature chromatographic isolation of transient intermediates of a multi‐step cycle: Purification of the substrate‐bound oxy‐ferrous cytochrome P450.png)
FEBS LettersOAAI
FEBS LettersIF0
OAAI分享收藏
查看更多内容