SC²S Colloquium - October 1, 2012

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Date: October 8, 2012
Room: 01.07.014
Time: 1:30 pm, s.t.


Leo Ng: Multifidelity Uncertainty Propagation for Optimization Under Uncertainty

In this seminar, I will first present a general overview of uncertainty quantification (UQ), including sources of uncertainties and types of UQ problems. I will then present my research in optimization under uncertainty. In optimization under uncertainty problems, computing the statistics that comprise the objective function and/or constraints many times during the search for the optimum can be computationally expensive. In many practical situations, low-fidelity models are available and can provide useful information about the outputs of the high-fidelity model at a lower cost. To take advantage of these low-fidelity models in a rigorous manner, I present a multifidelity approach to Monte Carlo estimation of parameters such as the mean and the variance of the high-fidelity model outputs based on the control variate variance reduction method. Numerical experiments have shown 84% reduction in computational cost relative to using the high-fidelity model alone.