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9.193 Lectures/texts in conference proceedings
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  • VII International Conference on Adaptive Modeling and Simulation (ADMOS 2015) Nantes, France 8–10 June, 2015
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Adaptive response surface approximation method for bayesian inference

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hdl:2117/334039

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Prudhomme, Serge
Bryant, Corey M.
Document typeConference report
Defense date2015
PublisherCIMNE
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
The 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.
CitationPrudhomme, S.; Bryant, C.M. Adaptive response surface approximation method for bayesian inference. A: ADMOS 2015. CIMNE, 2015, p. 90. 
URIhttp://hdl.handle.net/2117/334039
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