Simplex-stochastic collocation method with improved scalability

被引：14

作者：

Edeling, W. N. ^{[1
,2
]}

Dwight, R. P. ^{[2
]}

Cinnella, P. ^{[1
]}

机构：

[1] Arts & Metiers ParisTech, DynFluid Lab, 151 Blvd Hop, F-75013 Paris, France

[2] Delft Univ Technol, Fac Aerosp Engn, Kluyverweg 2, Delft, Netherlands

来源：

JOURNAL OF COMPUTATIONAL PHYSICS | 2016年 / 310卷

关键词：

Simplex-stochastic collocation method; Uncertainty quantification; Surrogate model; High-dimensional model reduction techniques; Uniform simplex sampling; PARTIAL-DIFFERENTIAL-EQUATIONS; RANDOM INPUT DATA; MODEL REPRESENTATIONS; TURBULENCE MODEL;

D O I：

10.1016/j.jcp.2015.12.034

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The Simplex-Stochastic Collocation (SSC) method is a robust tool used to propagate uncertain input distributions through a computer code. However, it becomes prohibitively expensive for problems with dimensions higher than 5. The main purpose of this paper is to identify bottlenecks, and to improve upon this bad scalability. In order to do so, we propose an alternative interpolation stencil technique based upon the Set-Covering problem, and we integrate the SSC method in the High-Dimensional Model-Reduction framework. In addition, we address the issue of ill-conditioned sample matrices, and we present an analytical map to facilitate uniformly-distributed simplex sampling. (C) 2016 Elsevier Inc. All rights reserved.

引用

页码：301 / 328

页数：28

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