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dc.contributor.authorDoostan, Alireza
dc.contributor.authorValidi, AbdoulAhad
dc.contributor.authorIaccarino, Gianluca
dc.date.accessioned2016-02-25T13:50:32Z
dc.date.available2016-02-25T13:50:32Z
dc.date.issued2013-08
dc.identifier.citationDoostan A, Validi A, Iaccarino G (2013) Non-intrusive low-rank separated approximation of high-dimensional stochastic models. Computer Methods in Applied Mechanics and Engineering 263: 42–55. Available: http://dx.doi.org/10.1016/j.cma.2013.04.003.
dc.identifier.issn0045-7825
dc.identifier.doi10.1016/j.cma.2013.04.003
dc.identifier.urihttp://hdl.handle.net/10754/598981
dc.description.abstractThis work proposes a sampling-based (non-intrusive) approach within the context of low-. rank separated representations to tackle the issue of curse-of-dimensionality associated with the solution of models, e.g., PDEs/ODEs, with high-dimensional random inputs. Under some conditions discussed in details, the number of random realizations of the solution, required for a successful approximation, grows linearly with respect to the number of random inputs. The construction of the separated representation is achieved via a regularized alternating least-squares regression, together with an error indicator to estimate model parameters. The computational complexity of such a construction is quadratic in the number of random inputs. The performance of the method is investigated through its application to three numerical examples including two ODE problems with high-dimensional random inputs. © 2013 Elsevier B.V.
dc.description.sponsorshipThe work of AD and AV was partially supported by the Department of Energy under Advanced Scientific Computing Research Early Career Research Award DE-SC0006402, the National Science Foundation grant DMS-1228359, and the Predictive Science Academic Alliance Program (PSAAP) at Stanford University. GI gratefully acknowledges financial support from KAUST under award AEA 48803.
dc.publisherElsevier BV
dc.subjectCurse-of-dimensionality
dc.subjectHydrogen oxidation
dc.subjectLow-rank approximation
dc.subjectNon-intrusive
dc.subjectSeparated representation
dc.subjectUncertainty quantification
dc.titleNon-intrusive low-rank separated approximation of high-dimensional stochastic models
dc.typeArticle
dc.identifier.journalComputer Methods in Applied Mechanics and Engineering
dc.contributor.institutionUniversity of Colorado at Boulder, Boulder, United States
dc.contributor.institutionStanford University, Palo Alto, United States
kaust.grant.numberAEA 48803


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