Dr.-Ing. Lukas Bruder


Research interests

  • Biomechanical Modeling and Simulation
  • Uncertainty Quantification
  • Inverse Problems
  • Surrogate modeling
  • Bayesian methods
  • Machine Learning & Statistical analysis

Academic background

04/2017 - 09/2021 Research associate, Mechanics & High Performance Computing Group, Technical University of Munich, Germany

08/2018 - 10/2018


Visiting researcher, Optimization & Uncertainty Quantification Department, Sandia National Labs, Albuquerque, USA

Master of Science (M.Sc.) in Mechanical Engineering, Technical University of Munich, Germany

PhD Thesis

  • Bruder, L. (2022): Biomechanical assessment of abdominal aortic aneurysm rupture risk and growth using clinical data: a probabilistic approach, https://mediatum.ub.tum.de/1639063

Peer-Reviewed Journal Articles

  • Bruder, L., Pelisek, J., Eckstein, H.-H., Gee, M.W. (2020): Biomechanical rupture risk assessment of abdominal aortic aneurysms using clinical data: a patient-specific, probabilistic framework and comparative study, PLOS ONE, 10.1371/journal.pone.0242097
  • Bruder, L., Gee, M.W., Wildey, T. (2020): Data-consistent Solutions to Stochastic Inverse Problems using a Probabilistic Multi-fidelity Method Based on Conditional Densities, International Journal for Uncertainty Quantification, 10.1615/Int.J.UncertaintyQuantification.2020030092
  • Bruder, L., Reutersberg, B., Bassilious, M., Schüttler, W., Eckstein, H.-H., Gee, M.W. (2019): Methoden der künstlichen Intelligenz in der vaskulären Medizin - Status quo und Ausblick am Beispiel des AAAs, Gefässchirurgie, 10.1007/s00772-019-00574-7
  • Bruder, L., Koutsourelakis, P.S. (2018): Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography, International Journal for Uncertainty Quantification10.1615/Int.J.UncertaintyQuantification.2018025837

Conference Contributions with Abstract

  • Bruder, L., Pelisek, J., Eckstein, H.-H., Gee, M.W.: A data-informed, patient-specific framework for the quantification of abdominal aortic aneurysm rupture risk, WCCM-ECCOMAS Virtual Congress, January 11-15, 2021
  • Bruder, L., Wildey, T.M., Pelisek, J., Eckstein, H.-H., Gee, M.W.: Parameter identification and uncertainty quantification for the predictive simulation of abdominal aortic aneurysm growth, UNCECOMP - International Conference on Uncertainty Quantification in Computational Sciences and Engineering, Crete, Greece, June 24-26 2019
  • Bruder, L., Pelisek, J., Eckstein, H.-H., Gee, M.W.: Towards fully patient-specific non-invasive rupture risk estimation of abdominal aortic aneurysms, ECCM - European Conference on Computational Mechanics, Glasgow, UK, June 11-15, 2018


  • Engineering Mechanics 1 Exercises
  • Engineering Mechanics 2 Exercises