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dc.contributor.authorPrudhomme, Serge
dc.contributor.authorBryant, Corey M.
dc.date.accessioned2020-12-04T14:41:30Z
dc.date.available2020-12-04T14:41:30Z
dc.date.issued2015
dc.identifier.citationPrudhomme, S.; Bryant, C.M. Adaptive response surface approximation method for bayesian inference. A: ADMOS 2015. CIMNE, 2015, p. 90.
dc.identifier.urihttp://hdl.handle.net/2117/334039
dc.description.abstractThe need for surrogate models and adaptive methods can be best appreciated if one is interested in parameter estimation using a Bayesian calibration procedure for validation purposes [1,2]. We extend our work on error decomposition and adaptive refinement for response surfaces [3] to the development of a surrogate model that can be utilized to estimate the parameters of Reynolds-averaged Navier-Stokes models. The error estimates and adaptive schemes are driven here by a quantity of interest and are thus based on the approximation of an adjoint problem. The desired tolerance in the error of the posterior distribution allows one to establish a threshold for the accuracy of the surrogate model. Particular focus is paid to accurate estimation of evidences to facilitate model selection.
dc.format.extent1 p.
dc.language.isoeng
dc.publisherCIMNE
dc.rightsOpen access
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi numèrica::Mètodes en elements finits
dc.subject.lcshFinite element method
dc.subject.lcshDigital computer simulation
dc.titleAdaptive response surface approximation method for bayesian inference
dc.typeConference report
dc.subject.lemacSimulació per ordinador digital
dc.rights.accessOpen Access
local.citation.contributorADMOS 2015
local.citation.startingPage90
local.citation.endingPage90


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