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Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluids simulation

hal.structure.identifierCertified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
dc.contributor.authorTANG, Kunkun
hal.structure.identifierCertified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
dc.contributor.authorCONGEDO, Pietro Marco
hal.structure.identifierInstitute of Mathematics University of Zurich
dc.contributor.authorABGRALL, Remi
dc.date.accessioned2024-04-04T03:17:54Z
dc.date.available2024-04-04T03:17:54Z
dc.date.created2015-06-07
dc.date.issued2015-06-07
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/194349
dc.description.abstractEnThe polynomial dimensional decomposition (PDD) is employed in this work for theglobal sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to amoderate to large number of input random variables. Due to the intimate structure between thePDD and the Analysis of Variance (ANOVA) approach, PDD is able to provide a simpler and moredirect evaluation of the Sobol’ sensitivity indices, when compared to the Polynomial Chaos expansion(PC). Unfortunately, the number of PDD terms grows exponentially with respect to the sizeof the input random vector, which makes the computational cost of standard methods unaffordablefor real engineering applications. In order to address the problem of the curse of dimensionality, thiswork proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model(i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed byregression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionalityfor ANOVA component functions, 2) the active dimension technique especially for second- andhigher-order parameter interactions, and 3) the stepwise regression approach designed to retainonly the most influential polynomials in the PDD expansion. During this adaptive procedure featuringstepwise regressions, the surrogate model representation keeps containing few terms, so thatthe cost to resolve repeatedly the linear systems of the least-square regression problem is negligible.The size of the finally obtained sparse PDD representation is much smaller than the one of the fullexpansion, since only significant terms are eventually retained. Consequently, a much less numberof calls to the deterministic model is required to compute the final PDD coefficients.
dc.language.isoen
dc.subject.enAtmospheric reentry
dc.subject.enAnalysis of Variance (ANOVA)
dc.subject.enGlobal sensitivity analysis
dc.subject.enPolynomial dimensional decomposition (PDD)
dc.subject.enRegression approach
dc.subject.enUncertainty quantification
dc.subject.enAdaptive sparse polynomial surrogate model
dc.title.enAdaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluids simulation
dc.typeRapport
dc.subject.halPhysique [physics]/Physique [physics]/Physique Numérique [physics.comp-ph]
dc.subject.halInformatique [cs]/Modélisation et simulation
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.type.institutionInria Bordeaux Sud-Ouest
bordeaux.type.reportrr
hal.identifierhal-01178398
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01178398v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2015-06-07&rft.au=TANG,%20Kunkun&CONGEDO,%20Pietro%20Marco&ABGRALL,%20Remi&rft.genre=unknown


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