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International Journal for Uncertainty Quantification
IF: 3.259 5-Year IF: 2.547 SJR: 0.531 SNIP: 0.8 CiteScore™: 1.52

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2014010147
pages 303-331

SPARSE MULTIRESOLUTION REGRESSION FOR UNCERTAINTY PROPAGATION

Daniele Schiavazzi
Mechanical and Aerospace Engineering Department, University of California, San Diego, California 92093, USA
Alireza Doostan
Aerospace Engineering Sciences Department, University of Colorado, Boulder, Colorado 80309-0429, USA
Gianluca Iaccarino
Department of Mechanical Engineering Institute for Computational Mathematical Engineering Stanford University Bldg 500, RM 500-I, Stanford CA 94305 - USA

ABSTRACT

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.


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