Model Reduction for Parametrized Systems
Modern discretization techniques for differential equations yield high dimensional simulation models, which require high computational effort for determining approximate solutions. This even gets more problematic, if many of such simulations are required, e.g. for parametrized problems. Such settings can be parameter studies, interactive simulations, parameter identification problems, statistical investigations, etc.
For such problems, efficient techniques for dimensionality reduction are desirable. In addition to fast algorithms, also error quantification is crucial. Methods for this can be found and are developed in the fields of Reduced Basis (RB) techniques for parametrized partial differential equations and Model Order Reduction (MOR) for parametrized dynamical systems. On the present website, we present our collaborative work on these questions.
- 23.05.2014: The registration deadline for the RB Summer School 2014 has been extended to June 7th, 2014.
- 23.04.2014: Version 0.2.0 of the Python-based model reduction library pyMOR has just been released. For more information, visit http://pymor.org
- 15.04.2014: There is an open position for a professorship in Scientific Computing at TU München, Germany. Deadline for applications is May 5, 2014.