Welcome to the Professorship for Data-driven Materials Modeling
We strongly believe that the rapid advances in data sciences and machine learning should not leave physical modeling untouched. The group promotes a data-driven perspective for carrying out engineering analysis and design tasks, in calibrating and validating computational models, but also in discovering new ones that are predictive of multiscale behavior. We employ a probabilistic-Bayesian mindset to achieve the fusion of data and models and to address fundamental challenges relating to dimensionality reduction & model compression and the discovery of salient patterns in the presence of small data.
Please visit the group's github repository for code related to our most recent research activities
