application deadline 28.11.2021 [weiterlesen]

Abstract:  One part of the validation process of electric engines must check for thermal aging and damage of their components due to the high temperatures to which they are exposed. This way, the thermal requirements of the machine can be defined, and specific minimum service life can be guaranteed.… [weiterlesen]

Abstract: Neural networks excel at finding patterns in large amounts of data, yet they struggle to learn the basic laws of physics. Applying the methods of machine learning to build accurate models of the world thus requires a strong inductive bias, e.g. a notion of symmetry, invariances or… [weiterlesen]

Abstract: Machine Learning (ML) is widely utilized in various fields to solve problems and assist research. In the field of medical image synthesis, Deep Learning (DL) shows great potential. Recently, different kinds of frameworks of Generative Adversarial Network (GAN) have been developed.… [weiterlesen]

Abstract The availability of high-performance computational resources have increased steadily, but we are still far form the capacity to perform high-fidelity simulations for turbulent flows in real-words applications. Thus, we still rely on computationally cheaper surrogates like Reynolds-Averaged… [weiterlesen]

Our group participates in the project consortium  "LeBeDigital: Life cycle of concrete - ontology development for the concrete production process chain" which is part of the first round of MaterialDigital call for proposals,funded by the BMBF. [weiterlesen]

Abstract: Gaussian processes are a well-studied stochastic machine learning method that has proven to be useful in many areas of application. They are flexible due to their non- parametric approach and are able to quantify the uncertainty of their predictions. This work describes the basic… [weiterlesen]

Our paper on "Physics-aware, probabilistic model order reduction with guaranteed stability" has been accepted to #ICLR2021.  This year's acceptance rate was 29%. Congratulations to Sebastian Kaltenbach for putting in all the hard work! More details can be found… [weiterlesen]

More details here Github repository here [weiterlesen]

Abstract:  In this thesis, we try to find a probabilistic Koopman-based representation for dynamical systems. Therefore we apply the framework of [Pan et al 2019], where the author successfully found such representation as a residual improvement of the powerful Dynamic Mode Decomposition technique.… [weiterlesen]