Members of the group


  • Sebastian Kaltenbach, "Physics-aware, probabilistic machine learning in the Small Data regime", Ph.D. 2023.
  • Maximilian Rixner, "Physics-Informed and Data-Driven Probabilistic Modeling of Materials Systems", Ph.D. 2023.
  • Luca Berardocco, "Hybridizable Discountinous Galerkin Methods for time-domain electromagnetic diffusion in underground strata formation", Ph.D. 2021.
  • Markus Schöberl, "Probabilistic Machine Learning Strategies for Coarse-Graining of Molecular Dynamics at Equilibrium” , Ph.D., 2020.
  • Constantin Grigo, "Physics-Aware, Bayesian Machine Learning Models for Uncertainty Quantification of High-Dimensional Systems in the Small Data Regime -  Applications in Random Media” , Ph.D., 2020.
  • Isabell Franck, "Sparse Variational Bayesian algorithms for large-scale inverse problems with applications in biomechanics", Ph.D., 2017.