Reduced Basis Methods for Parametric Problems

Jun.-Prof. Bernard Haasdonk, University of Stuttgart

28 Jul 2014, 16:15–17:45; Location: S2|17-103

In this presentation we will address different aspects concerning Reduced Basis (RB) methods for parametric partial differential equations (PDEs). This class of model reduction techniques enables rapid solution of parametric problems in the real-time or many-query context and has gained quite some interest and wide development over the last decade After presenting the fundamentals for stationary elliptic problems (affine parametric assumption, offline-online decomposition, greedy sampling, rigorous certification by efficient a-posteriori error bounds), we will put an emphasis on basis generation, including also instationary problems (Greedy and POD-Greedy procedures). Despite their sampling-based nature, these methods can be proven to be quasi-optimal in a rigorous approximation theoretic sense. For complex problems, also adaptive extensions can be devised. Some sample applications include nonlinear transport problems, multiscale settings or parameter optimization scenarios.

Category: CE Seminar


Technische Universität Darmstadt

Graduate School CE
Dolivostraße 15
D-64293 Darmstadt

Phone+49 6151/16-24401
Fax -24404

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