Mathematical Algorithmic Optimization
Major research interests
Efficient algorithms for optimal control and optimization problems, in particular for problems with mixed-integer control functions and variables and considering model identification from data, uncertainty, and real-time aspects.
Main application areas are chemical engineering and medicine.
Possible research projects (not exhaustive)
- Multi-step separation systems for complex mixtures arising from depolymerization or pyrolysis processes
(Collaboration partners: apl. Prof. Lorenz, Prof. Sundmacher, Prof. Kienle) - Uncertain multi-objective mixed-integer nonlinear optimization
(Collaboration partners: Prof. Mostaghim, Prof. Sundmacher) - Optimal control of recurrent processes
(Collaboration partners: Prof. Benner, Prof. Kienle) - Learning hybrid models from data
(Collaboration partners: Prof. Benner, Dr. Nüske, Prof. Mostaghim, Prof. Sundmacher) - Model and complexity reduction
(Collaboration partners: Prof. Benner, Dr. Feng, Prof. Richter)
Current doctoral researchers in the IMPRS ProEng program
Julius Martensen
PhD project since November 2020: Scientific Machine Learning : Physics Informed Machine Learning in Technical Systems
External doctoral researcher in the IMPRS ProEng program
IMPRS ProEng Alumnae and Alumni
Jennifer Uebbing
PhD thesis: Optimization of Power-to-Methane Processes with Respect to Exergy on System Level
(May 7, 2021)
(May 7, 2021)
