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dc.contributor.authorPrudhomme, Serge
dc.date.accessioned2017-06-05T08:35:49Z
dc.date.available2017-06-05T08:35:49Z
dc.date.issued2015-01-07
dc.identifier.urihttp://hdl.handle.net/10754/624111
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. We extend here our latest work on error decomposition and adaptive refinement for response surfaces to the development of surrogate models that can be substituted for the full models 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. We will focus in particular to the accurate estimation of evidences to facilitate model selection. The methodology will be illustrated on the Spalart-Allmaras RANS model for turbulence simulation.
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/4ab1cbe3c6c940cebe5a866a2d51bfe91d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
dc.titleAdaptive Surrogate Modeling for Response Surface Approximations with Application to Bayesian Inference
dc.typePresentation
dc.conference.dateJanuary 6-9, 2015
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
dc.conference.locationKAUST
dc.contributor.institutionÉcole Polytechnique de Montréal
refterms.dateFOA2018-06-14T05:02:53Z


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