Speaker:Karen Wilcox, Massachusetts Institute of Technology
Title: Model reduction for systems with high-dimensional parameter spaces

Abstract: Recent advances in projection-based model reduction methods for nonlinear and parametrically varying systems have opened up a broad new class of potential applications. Problems with large parameter dimension present a significant opportunity for model reduction to accelerate solution of large-scale systems with applications in optimization, inverse problems and uncertainty quantification. However, large parameter dimension also poses a significant challenge, since most model reduction methods rely on sampling the parameter space to build the reduced-space basis. This talk highlights recent progress on model reduction for large-scale problems with many parameters. Our approaches use a goal-oriented philosophy combined with optimization methods to guide the selection of samples over the parameter space in an adaptive manner. We also show how reduced basis approximations of the state space can be extended to reduce the dimension of the parameter space. We demonstrate our methods in the context of applications in optimization, inverse problems and uncertainty quantification with a variety of engineering examples.

Time: Monday, November 12, 2012, 4:30-5:30 p.m.

Place: Johnson Center, Room A

Department of Mathematical Sciences
George Mason University
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Fairfax, VA 22030-4444
http://math.gmu.edu/
Tel. 703-993-1460, Fax. 703-993-1491