Efficient Collocational Approach for Parametric Uncertainty Analysis

查看全文 作　　者：Dongbin [1]Xiu

机构地区：[1]Department of Mathematics,Purdue University,West Lafayette,IN 47906,USA.

出　　处：《Communications in Computational Physics》索引2007年第2卷第2期,共17页

摘　　要：A numerical algorithm for effective incorporation of parametric uncertainty into mathematical models is presented.The uncertain parameters are modeled as random variables,and the governing equations are treated as stochastic.The solutions,or quantities of interests,are expressed as convergent series of orthogonal polynomial expansions in terms of the input random parameters.A high-order stochastic collocation method is employed to solve the solution statistics,and more importantly,to reconstruct the polynomial expansion.While retaining the high accuracy by polynomial expansion,the resulting“pseudo-spectral”type algorithm is straightforward to implement as it requires only repetitive deterministic simulations.An estimate on error bounded is presented,along with numerical examples for problems with relatively complicated forms of governing equations. 关键词：Collocation methods pseudo-spectral methods stochastic inputs random differential equations uncertainty quantification.

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Efficient Collocational Approach for Parametric Uncertainty Analysis

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