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Multi-model and model structural uncertainty quantification with applications to climate science

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UNQW03 - Reducing dimensions and cost for UQ in complex systems

A common approach to quantifying the uncertainty in computer model predictions is to calibrate their tuning parameters to observational data. However, the largest uncertainties may not lie in the models' parameters, but rather in their “structures”: modelers make different choices in numerical schemes, physics approximations, sub-grid closures, and included processes. These choices result in different models that all claim to represent the same system dynamics, but may disagree in their predictions. This talk is aimed at presenting concepts and motivation concerning how to address such multi-model or structural uncertainty challenges. I present three methods. The first method, Bayesian multi-model combination, converts structural uncertainties in multiple computer models into parametric uncertainties within a reduced model. A hierarchical Bayesian statistical approach combines these parametric uncertainties into a single distribution representing multi-model uncertainty, which can be updated with observational constraints to dynamically bias-correct the multi-model ensemble. The second method uses system identification techniques to learn the governing equations of a PDE system. A non-intrusive model reduction approach is developed to rapidly explore uncertainties in alternate model structures by perturbing the learned dynamics. The third method is aimed at integrated uncertainty problems that require propagating uncertainties through multiple system components. It constructs a Bayesian network or graphical model where each node in the network quantifies uncertainties in a particular physical process, which can be informed by multiple types of model and data.

This talk is part of the Isaac Newton Institute Seminar Series series.

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