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.
- 28.06.2016: Version 4.0 of emgr (empirical gramian framework) has been released. See http://gramian.de for more information.
- 12.04.2016: The 6th Reduced Basis Summer School is organized by the AG Benner from the MPI Magdeburg. It will take place from the 4th to the 7th of October 2016; registration deadline: 17th of June 2016.
- 12.04.2016: A postdoctoral research position in Numerical Analysis and Scientific Computing (with applications) is open at SISSA, International School for Advanced Studies, Mathematics Area, mathLab division, Trieste, Italy. Further information are given in this document.