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dc.contributor.authorKnio, Omar
dc.date.accessioned2017-06-08T06:32:29Z
dc.date.available2017-06-08T06:32:29Z
dc.date.issued2016-01-06
dc.identifier.urihttp://hdl.handle.net/10754/624848
dc.description.abstractThis talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/2800314178804bc7be80a2476c1896aa1d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
dc.titleSurrogate based approaches to parameter inference in ocean models
dc.typePresentation
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.dateJanuary 5-10, 2016
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
dc.conference.locationKAUST
kaust.personKnio, Omar
refterms.dateFOA2018-06-14T03:41:15Z


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