Lukas Bruder, M.Sc.

Contact

Research interests

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

Peer-Reviewed Journal Articles

  • 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, accepted
  • 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., 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

Teaching

  • Engineering Mechanics 1 Tutorials (WS17/18, WS18/19, WS19/20)
  • Engineering Mechanics 2 Tutorials (SS17, SS18, SS19, SS20)

Supervised student projects / Theses

  • Input dimensionality reduction of parameterized computational models using active subspaces, Bachelor's Thesis (2020)
  • Development and investigation of a parameterized artificial AAA model, Bachelor's Thesis (2020)
  • Geometrically Nonlinear Topology Optimization, Master's Thesis (2020)
  • Variance-based global sensitivity analysis using Sobol’s method, Bachelor's Thesis (2019)
  • Krankheitsbild Aortenaneurysma - computergestützte Risikobewertung, TUMKolleg Forschungsarbeit (2018)
  • Implementierung und Validierung verschiedener Ansätze zur Vermeidung von Locking-Effekten in dünnwandigen, inkompressiblen Strukturen, Semester Thesis (2018)
  • Segmentation of Abdominal Aortic Aneurysms with Artificial Neural Networks & Methods of Deep Learning, Master's Thesis (2018)
  • AAA Parameter Regression using Bayesian Machine Learning, Bachelor's Thesis (2018)
  • Towards automated segmentation of abdominal aortic aneurysm CT data using convolution neural networks, MSE Research Internship (2017)
  • Untersuchung des Einflusses des Finite-Element-Netzes auf die Spannungsverteilung in abdominalen Aortenaneurysmen, MSE Research Internship (2017)

Academic background

since 04/2017 Research associate, Mechanics & High Performance Computing Group, Technische Universität München, Germany

08/2018 - 10/2018

02/2017

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

Master of Science (M.Sc.) in Mechanical Engineering, Technische Universität München, Germany