Research
The group “Numerical Mathematics and Control” conducts research in Nonlinear Dynamical Systems, Numerical Integration and Optimal Control. Our research focus lies in the development and analysis of structure-preserving simulations and optimal control methods for mechanical, electrical and hybrid systems, with a wide range of application areas including astrodynamics, drive technology and robotics.
Find out more about our research:
- Structure preserving integration
- Optimal Control
- Learning of Hamiltonians, Lagrangians, and Symmetries
- Research Project PhoQS
- Symplectic discretizations for optimal control problems in mechanics (DFG)
- Completed Research Projects
More information can be found on the research page.
Teaching
Our research group is looking to supervise master student projects! On the page teaching you will find two ideas for possible research directions that are suitable as master thesis projects. If you are interested, do not hesitate to contact us. Do not be put off if you do not understand the details of the project descriptions. Your willingness to learn new concepts and your passion are the most important factors.
Publications
2025
Multiphoton, multimode state classification for nonlinear optical circuits
D. Kopylov, C. Offen, L. Ares, B.E. Wembe Moafo, S. Ober-Blöbaum, T. Meier, P. Sharapova, J. Sperling, (n.d.).
Machine learning of continuous and discrete variational ODEs with convergence guarantee and uncertainty quantification
C. Offen, (n.d.).
2024
A new Lagrangian approach to control affine systems with a quadratic Lagrange term
S. Leyendecker, S. Maslovskaya, S. Ober-Blöbaum, R.T.S.M. de Almagro, F.O. Szemenyei, Journal of Computational Dynamics 0 (2024) 0–0.
Learning of discrete models of variational PDEs from data
C. Offen, S. Ober-Blöbaum, Chaos 34 (2024).
Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification
C. Offen, (n.d.).
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