Minisymposium Presentation

In areas for which vast amounts of data are available Machine learning and artificial intelligence techniques had a tremendous success, especially when mathematical models are lacking. Instead, engineering tools in general and computational fluid dynamics tools in particular rely on first-order principals that directly enable to describe and investigate system behavior. However, such tools are far from perfect and suffer several short-comings, e.g. computational bottlenecks once a massive amount of simulations is required or the problem of deriving accurate turbulence models to describe small scale turbulent behavior. Machine learning techniques are generally regarded as a possibility to enhance and complement first-order based numerical simulation tools to circumvent these shortcomings. Following this ambition, the Center for Computer Applications in AeroSpace Science and Engineering department of the German Aerospace Center investigates scientific machine learning techniques in close connection to established numerical simulation tools as well as industrial needs. This presentation will provide an insight into previous and current activities within the department covering topics from purely data-driven approaches to the incorporation of physical knowledge into models specifically highlighting challenges that arise when looking at large-scale industrial configuration and integration into existing, traditional workflows.