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Technische Universität München

Gil Robalo Rei, M.Sc.

Contact

  • Room 1228
  • Email: gil.rei@tum.de
  • Phone: +49 (0) 89 289 15240
  • Fax: +49 (0) 89 289 15301

Research Interests

  • Uncertainty Quantification
  • Bayesian Methods
  • Inverse Problems

Articles in peer-reviewed international journals

  • Maximilian Dinkel, Carolin M. Geitner, Gil Robalo Rei, Jonas Nitzler, Wolfgang A. Wall: Solving Bayesian Inverse Problems With Expensive Likelihoods Using Constrained Gaussian Processes and Active Learning, (2024), Inverse Problems, Volume 40.9, 095008, DOI

  • Hervas-Raluy S., Wirthl B., Enrique Guerrero P., Robalo Rei G., Nitzler J., Coronado E., Font de Mora J., Schrefler B. A., Gomez-Benito M. J., Garcia-Aznar J. M., Wall W.A. (2023), Tumour growth: An approach to calibrate parameters of a multiphase porous media model based on in vitro observations of Neuroblastoma spheroid growth in a hydrogel microenvironment, Computers in Biology and Medicine, 106895. doi:10.1016/j.compbiomed.2023.106895

International Conference Contributions

  • G. Robalo Rei, C.P. Schmidt, W.A. Wall: Optimisation under uncertainty of tortuosity for all-solid-state batteries based on variational particle size distributions, UNCECOMP 2023, 11.-14.06.2023, Athens, Greece
  • G. Robalo Rei, J. Nitzler, W.A. Wall: Black Box Variational Inference for Bayesian Inverse Problems with Complex Forward Models, 3rd IMA Conference on Inverse Problems, 03.-05.05.2022, Edinburgh, Scotland

Supervised student projects / theses

  • J. Rehfeldt: Bayesian Analysis of Spatial Activation Distributions in Skeletal Muscle Models, Bachelor's Thesis, 2024 (supervised together with Laura Engelhardt)
  • D. Michalke: Simulationsgestützte Topologieoptimierung von Festkörperelektrode, Research Internship, 2023 (supervised together with Christoph Schmidt)
  • D. Still: Adjoint based Bayesian Inference of Random Fields with Hamiltonian Monte Carlo Samplers, Master's Thesis, 2023 (supervised together with Maximilian Dinkel)
  • J. Schätz: Comparison of Multifidelity Methods for Uncertainty Quantification, Term Paper, 2022
  • K. Eberl: Implementation of a Classifier for Solver Convergence, Visualization Lab, 2022
  • B. Goderbauer: Runtime VTU Field Visualization in QUEENS, Visualization Lab, 2022
  • P. Jakobs: Surrogate-based uncertainty quantification in all-solid-state batteries, Bachelor's Thesis, 2022
  • M. Dinkel: Investigations of a Mesh-Free Kernel Based Solution Strategy for Nonlinear PDEs, Master's Thesis, 2022 (supervised together with Jonas Nitzler)

Education

  • Since 2021 Research Associate at the Institute for Computational Mechanics (Lehrstuhl für Numerische Mechanik), Technische Universität München, Germany
  • 2021 Master of Science (M.Sc.), Mechanical Engineering, Technische Universität München, Germany
  • 2018 Bachelor of Science (B.Sc.), Mechanical Engineering, Technische Universität München, Germany
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