The Data Science for Engineering group focuses on the development of data-driven and machine learning methods in the context of engineering. In particular, we address the question how data from various sources may be used for the analysis, real-time control and optimization of complex systems. The latter case also includes the simultaneous treatment of multiple conflicting objectives such as maximizing the productivity while minimizing the consumption of resources.
Specifically, we perform research in the following areas
- Data-driven model reduction, optimization and control of complex dynamical systems
- Koopman Operator-based approaches
- Machine Learning
- Optimization for machine learning
- Multiobjective optimization
- Acceleration of algorithms by structure exploitation
- Applications in the area of machine learning (e.g., sparse regression & neural network training)
- Efficent solution of expensive problems using model order reduction
Click here for more details on ongoing projects or publications.