.png){kind=icon}
返回
Finite dimensional models for random functions
.png){kind=icon}
OA
AI
DOI：10.1016/j.jcp.2018.09.029.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Truncated Karhunen-Loeve (KL) representations are used to construct finite dimensional (FD) models for non-Gaussian functions with finite variances. The second moment specification of the random coefficients of these representations are enhanced to full probabilistic characterization by using translation, polynomial chaos, and translated polynomial chaos models, referred to as T, PC, and PCT models. Following theoretical considerations on KL representations and T, PC, and PCT models, three numerical examples are presented to illustrate the implementation and performance of these models. The PCT models inherit the desirable features of both T and PC models. It approximates accurately all quantities of interest considered in these examples. (C) 2018 Elsevier Inc. All rights reserved.
Keyword:
Convergence of random series
Karhunen-Loeve series
Optimization
Polynomial chaos
Translation vectors
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
.png){kind=icon}
分享收藏
Solving elliptic boundary value problems with uncertain coefficients by the finite element method: the stochastic formulation通过有限元方法解决具有不确定系数的椭圆边值问题: 随机公式
PREAI
分享收藏
查看更多内容