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LANS Seminar
May 31, 2023 @ 10:30 - 11:30 CDT
Seminar Title: Multi-Fidelity Uncertainty Quantification
Speaker: John Jakeman, Principal Member of Technical Staff, Sandia National Laboratories
Date/Time: May 31, 2023 / 10:30 AM – 11:30 AM
Location: See meeting URL on the cels-seminars website (requires Argonne login)
Host: Ahmed Attia
Description: Recent advances in computational power and numerical algorithms have enabled revolutionary prediction of complex multi-scale multi-physics. However, because of their significant computational cost, it remains challenging to use these new high-fidelity (high-accuracy) for uncertainty quantification (UQ), which requires repeated evaluation of a model. Addressing this core challenge requires utilizing multiple simulation models and experiments of varying cost and accuracy. This talk will provide an overview of the multi-fidelity (MF) strategies for combining limited high-fidelity data with a greater amount of lower-fidelity data to substantially increase the accuracy of uncertainty estimates for a limited computational budget. Focus will be given to multi-fidelity quadrature methods that leverage the correlation between different models, arising from varying numerical discretizations and/or idealized physics, to reduce the cost of computing statistical estimators of uncertainty. Initial discussion will contrast MF methods that assume a hierarchy of models ordered by accuracy per unit costs, e.g. multi-level Monte Carlo (MLMC) [1], with methods that can be applied to un-ordered model ensembles, e.g approximate control variates (ACV) [2] and multi-level best linear unbiased estimators (ML-BLUE) [3]. The talk will then present recent developments in the latter class of non-hierarchical methods. Specifically, we will show that ACV and ML-BLUE are equivalent and present a new method for estimating uncertainty that uses multi-arm bandits to balance the cost of computing the correlation between models (exploration), needed for ACV and ML-BLUE, with the cost of computing the MF estimate of uncertainty (exploitation); the exploration cost is typically ignored by existing methods. The talk will conclude with some vignettes demonstrating the efficacy of MF quadrature on applications in plasma physics and ice-sheet modeling.
Bio: Dr John Jakeman is a Principal Member of Technical Staff at Sandia National Laboratories in Albuquerque. He received a Bachelor of Science and a Ph.D. in Mathematics from the Australian National University. John focuses on the development of methods that improve simulation-aided decision making by making efficient predictions from simulation and experimental data with quantified uncertainty. He has published extensively on topics including surrogate modelling, sensitivity analysis, optimal experimental design, and multi-fidelity uncertainty quantification. John is also the founding developer of PyApprox which is an open-source Python toolbox for probabilistic data and model analysis.
See all upcoming talks at https://www.anl.gov/mcs/lans-seminars
Please note that the meetng URL for this event can be seen on the cels-seminars website, which requires an Argonne login.