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Technical University of Munich

Maximilian Dinkel, M.Sc.

Contact

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

Teaching

Research Interests

  • Bayesian Methods
  • Bayesian Optimal Experimental Design
  • Inverse Problems
  • Gaussian Processes

Publications

  • Maximilian Dinkel, Gil Robalo Rei, Wolfgang A. Wall: Dynamic learning rate decay for stochastic variational inference, (2025), Machine Learning: Science and Technology, Volume 6.4, 045041, DOI
  • 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

International Conference Contributions with Abstract

  • Maximilian Dinkel, Wolfgang A. Wall: Towards Higher Information Gain from Bayesian Calibration in Real-World Engineering Applications, UNCECOMP 2025, 15.-18.06.2023, Rhodes, Greece
  • Maximilian Dinkel, Gil Robalo Rei, Wolfgang A. Wall: Dynamic Learning Rate Decay for Stochastic Variational Inference, 4th IMA Conference on Inverse Problems from Theory to Application, 11.-13.09.2024, Bath, UK
  • Maximilian Dinkel, Carolin M. Geitner, Wolfgang A. Wall: An approach for solving inverse problems using constrained Gaussian processes, UNCECOMP 2023, 11.-14.06.2023, Athens, Greece

Supervised student projects / theses

  • J. Köckeritz: Solving Bayesian Inverse Problems Using Constrained Gaussian Processes with Derivative Observations, Bachelor's Thesis, 2025
  • C. Speigner: Gradient-Accelerated Inverse Analysis of a Reduced-Order Human Lung Model, Term Paper, 2025, (supervised together with Benedikt Goderbauer)
  • D. Still: Adjoint based Bayesian Inference of Random Fields with Hamiltonian Monte Carlo Samplers, Master's Thesis, 2023 (supervised together with Gil Robalo Rei)

Education

  • Since 2022 Research Associate at the Institute for Computational Mechanics (Lehrstuhl für Numerische Mechanik), Technische Universität München, Germany
  • 2022 Master of Science (M.Sc.), Mechanical Engineering, Technische Universität München, Germany
  • 2019 Bachelor of Science (B.Sc.), Management and Technology, Technische Universität München, Germany
  • 2018 Bachelor of Science (B.Sc.), Mechanical Engineering, Technische Universität München, Germany

 

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