All aspects of life and engineering get increasingly connected, so get our models of the build environment: We follow on an interdisciplinary teaching concept and establish linkages to the courses of our partners in whenever possible.
As in research, our courses cover the intersection of physics-based modeling, numerics, machine learning and uncertainty quantification. Since data in engineering is usually limited, we learn how machine learning algorithms can be enriched with physical models.
Given some observational data, students will be confronted with the questions: What is useful to learn? What is save to learn? How about the physics? What is the range of applicability of a trained model?