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]

Abstract: Accurate modeling of physical systems is of great importance to engineers. In this thesis, we construct novel machine learning models based on the Neural ODE approach in Chen et al. (2018) and compare them to existing architectures. The evaluation is performed on multiple physical systems,… [weiterlesen]

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More details here Github repository here [weiterlesen]

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