The weekly SILO Seminar Series is made possible through the generous support of the 3M Company and its Advanced Technology Group


with additional support from the Analytics Group of the Northwestern Mutual Life Insurance Company

Northwestern Mutual

Optimal sampling in multifidelity Monte Carlo methods for uncertainty propagation

Ben Peherstorfer , Assistant Professor - UW–Madison

Date and Time: Feb 15, 2017 (12:30 PM)
Location: Orchard room (3280) at the Wisconsin Institute for Discovery Building


Abstract: In uncertainty propagation, coefficients, boundary conditions, and other key inputs of computational models are given as random variables and one is interested in estimating statistical moments of the corresponding model outputs. Estimating the moments with crude Monte Carlo can become prohibitively expensive in cases where a single model solve is already computationally demanding. We present multifidelity methods that leverage low-cost, inaccurate surrogate models for speedup and occasionally make recourse to the expensive high-fidelity model to establish unbiased estimators, even in the absence of error control for the surrogate models. We demonstrate our multifidelity methods for uncertainty propagation and rare event simulation on various numerical examples.