A framework for the stochastic inversion of Process-Structure-Property chain in the design of heterogeneous materials. More details: https://arxiv.org/abs/2408.01114 [read more]

We solve model-based Bayesian inverse problems without a forward model.We explore applications in elastography with the ultimate goal of enabling model-based diagnosis on hand-held devices by non-experts. More details here: https://arxiv.org/abs/2407.20697 [read more]

Abstract: The goal of inverse molecular design is the discovery of novel molecular structures that fulfill a set of target properties. In this project we explore the application of generative models and molecular graph representations for the development of solutions to the inverse molecular design… [read more]

We offer multiple opportunities for a M.Sc. thesis on several topics at the interface of Machine Learning and Physical Modeling. The projects would be contacted in close collaboration with Carl Zeiss AG. More details can be found here. If you are interested please send an Email to Prof.… [read more]

We are currently looking for M.Sc. students to work on the topic of "Inverse Materials Design".  This is an interdisciplinary topic that combines computational modeling of physical systems with adavnaced machine learning techniques. More details can be found here [read more]

Abstract: Random media and the process-structure-property chain generally define complex, high-dimensional and stochastic materials systems, posing a challenging setting for any prediction or optimization task. In this thesis, we pursue a Bayesian approach for learning and predicting the behavior… [read more]

Abstract: Solving high-dimensional, nonlinear systems is a key challenge in engineering and computational physics. We propose novel physics-aware machine learning models that rely both on physical knowledge as well as a small amount of data and are, after an initial training phase, able to solve… [read more]

While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials’ community, fewer efforts have taken into consideration uncertainties. Those arise from a multitude of sources and their quantification and integration in the inversion… [read more]