SPARSE MULTIRESOLUTION REGRESSION FOR UNCERTAINTY PROPAGATION

被引：13

作者：

Schiavazzi, Daniele ^{[1
]}

Doostan, Alireza ^{[2
]}

Iaccarino, Gianluca ^{[3
]}

机构：

[1] Univ Calif San Diego, Dept Aerosp Engn & Mech, La Jolla, CA 92093 USA

[2] Univ Colorado, Aerosp Engn Sci Dept, Boulder, CO 80309 USA

[3] Stanford Univ, Dept Engn Mech, Stanford, CA 94305 USA

来源：

INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION | 2014年 / 4卷 / 04期

基金：

美国国家科学基金会;

关键词：

uncertainty quantification; multiresolution approximation; compressive sampling; adaptive importance sampling; tree-based orthogonal matching pursuit; uncertain tuned mass damper; PARTIAL-DIFFERENTIAL-EQUATIONS; STOCHASTIC COLLOCATION METHOD; POLYNOMIAL CHAOS; SIGNAL RECOVERY; APPROXIMATION; BASES; MINIMIZATION; SYSTEMS;

D O I：

10.1615/Int.J.UncertaintyQuantification.2014010147

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The present work proposes a novel nonintrusive, i.e., sampling-based, framework for approximating stochastic solutions of interest admitting sparse multiresolution expansions. The coefficients of such expansions are computed via greedy approximation techniques that require a number of solution realizations smaller than the cardinality of the multiresolution basis. The effect of various random sampling strategies is investigated. The proposed methodology is verified on a number of benchmark problems involving nonsmooth stochastic responses, and is applied to quantifying the efficiency of a passive vibration control system operating under uncertainty.

引用

页码：303 / 331

页数：29

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