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Deterministic RBF Surrogate Methods for Uncertainty Quantification, Global Optimization and Parallel HPC Applications

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UNQW02 - Surrogate models for UQ in complex systems

Co-author: Antoine Espinet (Cornell University)

This talk will describe general-purpose algorithms for global optimization These algorithms can be used to estimate model parameters to fit complex simulation models to data, to select among alternative options for design or management, or to quantify model uncertainty. In general the numerical results indicate these algorithms do very well in comparison to alternatives, including Gaussian Process based approaches.. Prof. Shoemaker’s group has developed open source (free) PySOT optimization software that is available online (18,000 downloads) . The algorithms can be run in serial or parallel. The focus of the talk will be on SOARS , an Uncertainty Quantification method for using optimization-based sampling to build a surrogate likelihood function followed by additional sampling The algorithms builds a surrogate approximation of the likelihood function based on simulations done during the optimization search. Then MCMC is performed by evaluating the surrogate likelihood function rather than the original expensive-to-evaluate function. Numerical results indicate the SOARS algorithm is very accurate when compared to the posterior densities computed when using the expensive exact likelihood function. I also discuss an application to a model of the underground movement of a plume of geologically sequestered carbon dioxide. The uncertainty in the parameter values obtained from the MCMC analysis on the surrogate likelihood function can be used to assess alternative strategies for identifying a cost-effective plan that will most efficiently give a reliable forecast of a carbon dioxide underground plume. This includes joint work with David Ruppert, Antoine Espinet, Nikolay Bliznyuk, and Yilun Wang.

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