Model Order Reduction

Model Order Reduction

In order to get a deep insight into the underlying process, dynamics, structure or devices, modeling and simulation are unavoidable in many research and application fields. The resulting mathematical models are usually in the form of partial differential equations. To simulate such models, spatial (-time) discretization is necessary, which results in large-scale, complex systems with enormous number of equations. The simulation becomes time-consuming because of the large scale and complexity of the systems.

Developed from well-established mathematical theories and robust numerical algorithms, model order reduction (MOR) has been recognized as an efficient tool for fast simulation.


Model Order Reduction for Battery Models

Funded by: MPI/IMPRS
Funding period: since 03.2021
Contact: Shuwen Sun, Lihong Feng
Funded by: MPI
Funding period: since 11.2019
Contact: Jennifer Przybilla, Peter Benner

Partners: Prof. Dr. Peter Benner (MPI Magdeburg, OVGU), Prof. Dr. Sebastian Sager (OVGU), Prof. Dr. Kai Sundermacher MPI Magdeburg, OVGU)Prof. Dr. Martin Stoll (TU Chemnitz)
Industrial partners:  AVACON und BASF
Funded by: BMBF
Contact: Peter Benner, Lihong Feng, Shaima Monem
Funded by: MPI
Funding period: since 07/2019

Contact: Carmen Gräßle, Peter Benner 


Model order reduction for fuel cell models

Funded by: MPI
Funding period: since 03.2021
Partners: Tanja Vidakovic-Koch (MPI), Mian Ilyas Ahmad (NUST, Pakistan)
Contact: Lihong Feng

Concluded Projects

Partners: OvGU, Technische Hochschule Bingen
Funded by: MPI
Funding period: since 04.2017
Contact: Sridhar Chellappa, Lihong Feng

Partners: OvGU, Technische Hochschule Bingen, MPI Leipzig
Funded by:
Funding period:
Cleophas Kweyu

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