Many problems in different engineering disciplines share similar challenges from the methodological perspective. They are typically characterized by high-dimensional state and parameter spaces, nonlinear dynamics and heterogeneous multi-modal and partly high-frequency state and condition observations, together with possibly noisy and limited data. The physics- based models are usually computationally very expensive and may not be fully representative of the underlying processes, since those may not even be completely understood. The applications typically also require interpretability, extrapolation ability and the quantification of the underlying uncertainties of the predictions/estimations. In many cases, the collected data is not fully representative of all possible conditions and is lacking labels that are required for training. While the problems in different disciplines in sciences and engineering appear very different, innovative AI algorithms that span across domains enable fast, efficient and robust solutions to these problems.

In this track, we aim to showcase the potential of AI methods for engineering applications, discuss the current state of research and outstanding issues, and point out the most promising research directions. By gathering well-known speakers and young researchers from different engineering disciplines, we will be able to recognize similarities and differences in the research needs of the various areas and promote exchange and discussion on common grounds.