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-drive parametric reduced-order modelling.
Further information on the MOR team in the CSC group can be found here.

Current PhD Students in the IMPRS program

Sridhar Chellappa

Sridhar Chellappa

Website
PhD project: Adaptivity and a posteriori Error Estimation for Frequency- and Time-Domain Model Order Reduction
Harshit Jyothsnaben Kapadia

Harshit Jyothsnaben Kapadia

Website
PhD project: Reduced-order modeling for large-scale computational fluid dynamic problems
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