The load carrying capability of structures such as bridges, aircraft fuselages, etc. is subjected to a stochastic scatter, since the properties of these structures (material parameters, geometry, …) have inherent uncertainties. One of our main research areas is the prediction of this variation using probabilistic simulations (e.g. Monte Carlo method). Such simulations can be extremely computationally intensive since a large number of calculations is carried out with varying values of the scattering properties. One way of speeding up such simulations that currently receives much attention is the use of surrogate models (a.k.a. meta models). These surrogate models are trained with a much smaller number of simulation results than a classic Monte Carlo simulation would require. In our research we use surrogate models such as neural networks, Kriging and polynomial chaos expansions to efficiently predict the scattering of fiber composite structures.


  • Uncertainty quantification with surrogate models
  • Simulation of random distortions
  • Machine learning applications to structural reliability


  • Research Associate, Structural Optimization for Lightweight Design, 2019 - present

    Hamburg University of Technology, Germany

  • Research & Development Engineer, 2016 - 2018

    AUSY Engineering GmbH, Hamburg

  • Visiting Researcher, 2018

    Arts et Métiers ParisTech - École Nationale Supérieure d'Arts et Métiers, Bordeaux, France

  • Visiting Researcher, 2017

    Leibniz Universität Hannover, Germany