A sparse multiresolution stochastic approximation for uncertainty quantification

被引：5
|
作者：
Schiavazzi, D. [1 ]
Doostan, A.
Iaccarino, G.
机构：
[1] Univ Padua, Dipartimento Matemat, I-35100 Padua, Italy
来源：
RECENT ADVANCES IN SCIENTIFIC COMPUTING AND APPLICATIONS | 2013年 / 586卷
关键词：
DIFFERENTIAL-EQUATIONS; POLYNOMIAL CHAOS;
D O I：
10.1090/conm/586/11634
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The present work proposes a novel sampling-based uncertainty propagation framework in which solutions are represented using a multiresolution dictionary. The coefficients of such an expansion are evaluated using greedy methodologies within the Compressive Sampling framework. The effect of various sampling strategies is investigated. The proposed methodology is verified on the Kraichnan-Orszag problem with one and two random initial conditions.
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页码：295 / +
页数：3
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