MULTISCALE MODEL REDUCTION FOR THE SOLUTION OF STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS WITH LOCALIZED SOURCES OF UNCERTAINTIES

被引：0

作者：

Safatly, Elias ^{[1
]}

Chevreuil, Mathilde ^{[1
]}

Nouy, Anthony ^{[1
]}

机构：

[1] Univ Nantes, LUNAM Univ, Ecole Cent Nantes, GeM,UMR CNRS 6183, Nantes, France

来源：

PROCEEDINGS OF THE ASME 11TH BIENNIAL CONFERENCE ON ENGINEERING SYSTEMS DESIGN AND ANALYSIS, 2012, VOL 1 | 2012年

关键词：

RANDOM DOMAINS; DECOMPOSITION;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The presence of numerous localized sources of uncertainties in stochastic models leads to high dimensional and multiscale problems. A numerical strategy is here proposed to propagate the uncertainties through such models. It is based on a multiscale domain decomposition method that exploits the localized side of uncertainties. The separation of scales has the double benefit of improving the conditioning of the problem as well as the convergence of tensor based methods (namely Proper Generalized Decomposition methods) used within the strategy for the separated representation of high dimensional stochastic parametric solutions.

引用

页码：197 / 203

页数：7

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