Model Reduction for Multiscale Lithium-Ion Battery Simulation
- Speaker: Prof. Dr. Mario Ohlberger
- Place: Seminar room 1.067, Building 20.30
- Time: 2.2.2017, 14:00 - 15:00
- Invited by: CRC 1173
Model reduction approaches for parameterized problems have seen tremendous development in recent years. A particular instance of projection based model reduction is the reduced basis (RB) method, which is based on the construction of low-dimensional approximation
spaces from snapshot computations, i.e. solutions of the underlying parameterized problem for suitably chosen parameter values.
In this talk we will particularly address model reduction for multiscale Lithium-Ion battery simulations as well as recent advances in localized model order reduction which are particularly well suited to treat large scale or heterogeneous multiscale scale problems. We derive suitable localized a posteriori error estimates against the underlying true solution of the parameterized problem and demonstrated how this error estimator can be used to overcome classical so called offline/online splitting though the newly developed concept of online enrichment. The resulting method only needs a very cheap
preparation step and the iteratively enriches localized snapshot spaces using the
localized a posteriori error information.
Joint work with Stephan Rave and Felix Schindler.