Uncertainty quantification using polynomial chaos expansion with points of monomial cubature rules

被引：40

作者：

Wei, D. L. ^{[1
]}

Cui, Z. S. ^{[1
]}

Chen, J. ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Natl Die & Mold CAD Engn Res Center, Shanghai 200030, Peoples R China

来源：

COMPUTERS & STRUCTURES | 2008年 / 86卷 / 23-24期

基金：

中国国家自然科学基金;

关键词：

Uncertainty quantification; Polynomial chaos expansion; Monomial cubature rules; Sampling points;

D O I：

10.1016/j.compstruc.2008.07.001

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper proposes an efficient method for estimating uncertainty propagation and identifying influence factors contributing to uncertainty. In general, the system is dominated by some of the main effects and lower-order interactions due to the sparsity-of-effect principle. Therefore, the construction of polynomial chaos expansion with points of monomial cubature rules is particularly attractive in dealing with large computational model. This approach has advantages over many others as it needs far fewer sampling points for multivariate models and all of the points can be sampled. The proposed approach is validated via two mathematical functions and an engineering problem. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：2102 / 2108

页数：7

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