Omar Knio, King Abdullah University of Science & Technology

Surrogate based approaches to parameter inference in ocean models


This 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.

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