Beyond Wiener-Askey expansions: Handling arbitrary PDFs

被引：101

作者：

Wan, Xiaoliang ^{[1
]}

Karniadakis, George Em ^{[1
]}

机构：

[1] Brown Univ, Div Appl Math, Providence, RI 02912 USA

来源：

JOURNAL OF SCIENTIFIC COMPUTING | 2006年 / 27卷 / 1-3期

关键词：

uncertainty; polynomial chaos; stochastic differential equation;

D O I：

10.1007/s10915-005-9038-8

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper we present a Multi-Element generalized Polynomial Chaos (ME-gPC) method to deal with stochastic inputs with arbitrary probability measures. Based on the decomposition of the random space of the stochastic inputs, we construct numerically a set of orthogonal polynomials with respect to a conditional probability density function (PDF) in each element and subsequently implement generalized Polynomial Chaos (gPC) locally. Numerical examples show that ME-gPC exhibits both p- and h-convergence for arbitrary probability measures.

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收藏

页码：455 / 464

页数：10

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