Welcome to the Data Science for Engineering group
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.
Research
Our research focuses on the following areas
1. Data-driven model reduction, optimization and control of complex dynamical systems
- Koopman Operator-based approaches
- Machine learning for prediction and control
- Reinforcement learning
2. Multiobjective optimization
- Acceleration of algorithms by structure exploitation
- Efficient training algorithms for deep learning
- Applications in the area of machine learning (e.g., sparse regression & neural network training)
- Efficient solution of expensive problems using model order reduction
About us
Networks
News
Jun.-Prof. Dr. Sebastian Peitz
Data Science for Engineering
Room O4.213
Paderborn University
Pohlweg 51
33098 Paderborn
Pohlweg 51
33098 Paderborn