Model Reduction for Uncertainty Quantification and Optimization of Large-scale Systems

For many engineering systems, decision-making under uncertainty is an essential component of achieving improved system performance; however, uncertainty quantification and stochastic optimization approaches are generally computationally intractable for large-scale systems, such as those resulting from discretization of partial differential equations. This talk presents recent advances in model reduction methods --- which aim to generate low-dimensional, efficient models that retain predictive fidelity of high-resolution simulations --- that make possible the characterization, quantification and control of uncertainty in the large-scale setting.

Karen Willcox, Massachusetts Institute of Technology

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