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
Sridhar Chellappa
PhD project: Adaptivity and a posteriori Error Estimation for Frequency- and Time-Domain Model Order Reduction
