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.

Model order reduction (MOR), developed from well-established mathematical theories, robust numerical algorithms, and aided by machine learning, has been recognized as an efficient tool for fast simulation.

Projects

Adaptively Synthesized Surrogates for Reliable and Efficient Model Order Reduction
Funded by: MPI/IMPRS and MPI
Funding period: since 04.2017
Contact: Sridhar Chellappa, Lihong Feng more
Surrogate Modelling for Problems with Large-dimensional Parameter Spaces
Funded by: Tsinghua University and MPI
Funding period: 03.2024-07.2025
Contact: Chenzi Wang, Lihong Feng more
Parameter-dependent Time Sequence Prediction with Deep Learning
Funded by: MPI/IMPRS
Funding period: since 03.2021
Contact: Shuwen Sun, Lihong Feng more
Non-intrusive Surrogate Modelling
Funded by: MPI/IMPRS
Funding period: since 11.2019
Contact: Harshit Kapadia, Lihong Feng more
P2Chem: New mixed-integer optimization methods for efficient synthesis and flexible management from power-to-chemicals processes
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
more

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

Adaptive and data informed model order reduction for optimal control of complex systems
Funded by: MPI
Funding period: 07.2019-09.2021

Contact: Carmen Gräßle, Peter Benner 
  more
Damping Optimization of Mechanical Systems using the Reduced Basis Method
Funded by: MPI
Funding period: 11.2019-12.2023
Contact: Jennifer Przybilla, Peter Benner
  more
Adaptive Model Order Reduction for Parametric Nonlinear Systems
Partners: OvGU, Technische Hochschule Bingen
Funded by: MPI
Funding period: since 04.2017
Contact: Sridhar Chellappa, Lihong Feng


MOR in Process engineering, molecular simulations
Partners: OvGU, Technische Hochschule Bingen, MPI Leipzig
Funded by:
MPI/IMPRS
Funding period:
2014-2019
Contact
Cleophas Kweyu


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