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.
An important task in this context is the approximation of the system behavior from data to allow for efficient and reliable predictions. Depending on whether a mathematical model is available, either real data, simulation data, or a combination of both may be used. In contrast to many other application areas, reliability and security are of utmost importance in engineering, as system failures may have fatal consequences. The reliability of data-driven methods is thus of central importance.