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.
- 07.03.2016: The next Reduced Basis (and Friends) Summer School (RBSS) will take place at Hotel Hessenkopf, Goslar, Germany from 19th September to 22nd September 2017. Further information can be found here or directly on www.rbss2017.de.
- 21.02.2016: Open research position between SISSA, International School for Advanced Studies, Mathematics Area, mathLab, Trieste, Italy and FINCANTIERI spa, worldwide leader in ship constructions. Click here for details.
- 13.02.2016: Two new open positions as post-doc research associate at SISSA mathLab are available. Click here for details.