Model Reduction for Large-scale Systems

Model reduction entails the systematic generation of cost-efficient representations of large-scale systems that result, for example, from
discretization of partial differential equations. Considerable progress in model reduction methodologies for large-scale systems has
seen successful application to the fields of computational fluid dynamics, structural dynamics, and circuit design. In this talk, we
discuss recent developments in reduced models for optimal design, optimal control and inverse problem applications. In  such
cases---where the physical system must be simulated repeatedly---the availability of reduced models can greatly facilitate solution of the
optimization problem, particularly for real-time and/or large-scale applications.

Karen Willcox, Massachusetts Institute of Technology

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