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, P.M. Praegla, W.A. Wall: Enhancing Physical Properties of Powder Systems through Particle Size Distribution Optimization and Non-Differentiable Stochastic Models, 4rd IMA Conference on Inverse Problems, 11.-13.09.2024, Bath, United Kingdom
- 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
- E. Ekici: Towards an Extended Discrete Element Method for Electrochemical Simulations of Solid-State Batteries, Bachelor's Thesis, 2024 (supervised together with Christoph Schmidt)
- 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