Model Order Reduction
Major research interests
The research in model order reduction (MOR) team covers almost all the topics in this area, including but not limited to:
- Intrusive and non-intrusive MOR methods and algorithms for parametric linear or nonlinear, time-dependent or steady systems;
- MOR-based optimiation, control, uncertainty quantification, etc;
- Efficient error estimators for linear or nonlinear parametric systems;
- Adaptive scheme for automatic reduced-order model generation;
- Data-driven parametric reduced-order modelling based on deep learning.
Further information on the MOR team in the CSC group can be found here.
Current PhD Students in the IMPRS program
PhD project: Adaptivity and a posteriori Error Estimation for Frequency- and Time-Domain Model Order Reduction