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Technical University of Munich

Jonas Nitzler, M.Sc.

Contact

  • Room 1230
  • Email: jonas.nitzler@tum.de
  • Phone: +49 (0) 89 289 15257
  • Fax:     +49 (0) 89 289 15301

Research interests

  • Efficient Bayesian Multi-Fidelity Schemes for Complex Systems
  • Probabilistic Models and Uncertainty Quantification (UQ)
  • Physics Informed Machine Learning
  • Inverse Problems
  • QUEENS - A general purpose framework for Uncertainty Quantification, Physics-Informed Machine Learning, Bayesian Optimization, Inverse Problems and Simulation Analytics on distributed computer systems.

Current Pre-Prints

  • Jakob Richter, Jonas Nitzler, Luca Pegolotti, Karthik Menon, Jonas Biehler, Wolfgang A. Wall, Daniele E. Schiavazzi, Alison L. Marsden, Martin R. Pfaller:  Bayesian Windkessel calibration using optimized 0D surrogate models, 2024 (ArXiv: https://arxiv.org/abs/2404.14187)

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 
  • Silvia Hervas-Raluy, Barbara Wirthl, Pedro E. Guerrero, Gil Robalo Rei, Jonas Nitzler, Esther Coronado, Jaime Font de Mora, Bernhard A. Schrefler, Maria Jose Gomez-Benito, Jose Manuel Garcia-Aznar, Wolfgang A. Wall, 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, (2023), Computers in Biology and Medicine, Volume 159, 106895, DOI
  • J. Nitzler, J. Biehler, N. Fehn, P.-S. Koutsourelakis, W. A. Wall, A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Propagation in Complex Physical Simulations (2022), Computer Methods in Applied Mechanics and Engineering, Volume 400, Issue C, 115600, DOI
  • H. Willmann, J. Nitzler, S. Brandstaeter, W. A. Wall, Bayesian calibration of coupled computational mechanics models under uncertainty based on interface deformation, Adv. Model. and Simul. in Eng. Sci. 9, 24 (2022), DOI
  • B. Wirthl, S. Brandstäter, J. Nitzler, B. A. Schrefler, W. A. Wall, Global sensitivity analysis based on Gaussian-process metamodelling for complex biomechanical problems, (2022), Int J Numer Meth Biomed Engng., e3675, DOI
  • C. A. Meier, S. L. Fuchs, N. Much, J. Nitzler, R. W. Penny, P. M. Praegla, S. D. Pröll, Y. Sun, R. Weissbach, M. Schreter, N. E. Hodge, A. J. Hart, W. A. Wall, Physics-Based Modeling and Predictive Simulation of Powder Bed Fusion Additive Manufacturing Across Length Scales, Surveys for Applied Mathematics and Mechanics (GAMM Mitteilungen), Wiley Online Library (2021), DOI
  • J. Nitzler, C. Meier, K. W. Müller, W. A. Wall, N. E. Hodge, A Novel Physics-Based and Data-Supported Microstructure Model for Part-Scale Simulation of Laser Powder Bed Fusion of Ti-6Al-4V, Adv. Model. and Simul. in Eng. Sci. 8, 16 (2021), DOI
  • J. Biehler, M. Mäck, J. Nitzler, M. Hanss, P-S. Koutsourelakis, W. A. Wall, Multi-Fidelity Approaches for Uncertainty Quantification, Surveys for Applied Mathematics and Mechanics (GAMM Mitteilungen), Wiley Online Library (2019), DOI

International Conference Contributions with Abstract

  • J. Nitzler, P.-S. Koutsourelakis, W.A. Wall, Efficient high-dimensional Bayesian multi-fidelity inverse analysis for expensive legacy solvers (BMFIA), Applied Inverse Problems (AIP23), 04.09-08.09.2023, Göttingen, Germany
  • J. Nitzler, W.A. Wall, P.-S. Koutsourelakis, Efficient and high-dimensional Bayesian inverse analysis for large-scale, non-differentiable legacy codes, UNCECOMP 2023, 11.06-14.06.2023, Athens, Greece
  • J. Nitzler, W.A. Wall, P.-S. Koutsourelakis, Bayesian multi-fidelity inverse analysis (BMFIA) for expensive, non-differentiable, physics-based simulations in high stochastic dimensions, SIAM Conference on Computational Science and Engineering, 26.02.-03.03.2023, Amsterdam, The Netherlands
  • J. Nitzler, W. A. Wall, P.-S. Koutsourelakis, Bayesian Multi-Fidelity Inverse Analysis, Model Reduction and Surrogate Modeling (MORE), 19.09.-23.09.2022, Berlin, Germany
  • J. Nitzler, W. A. Wall, P.-S. Koutsourelakis, Bayesian Multi-Fidelity Inverse Analysis for Computationally Demanding Models in High Stochastic Dimensions, SIAM Conference on Uncertainty Quantification 2022, Atlanta, Georgia, U.S.
  • J. Nitzler, W. A. Wall, P.-S. Koutsourelakis, A Bayesian Multi-Fidelity Framework for the Efficient Solution of Inverse Problems in Large-Scale Biomechanical Problems, SIAM 2021 Conference on Computational Science and Engineering, 01-05 March, 2021
  • J. Nitzler, J. Biehler, P.-S. Koutsourelakis, W. A. Wall, Uncertainty Quantification in Fluid-Structure Interaction Exploiting Automatically Generated Cheap Approximators, 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP), Crete, Greece, 24-26 June, 2019

Supervised student projects / theses

  • Lea J. Häusel: Investigation of Bayesian Neural Networks to Enhance Bayesian Multi-Fidelity Inverse Analysis, Master's Thesis (completed 01.2024)
  • Jakob Richter: Multi-fidelity boundary condition tuning for cardiovascular fluid dynamics simulations under uncertainty, Master's Thesis (completed 10.2022, supervised together with Jonas Biehler and Martin Pfaller)
  • Maximilian Dinkel: A stabilized mesh-free Petrov-Galerkin approach interpreted as Bayesian kernel regression, Master's Thesis (completed 02.2022, supervised together with Gil Robalo Rei)
  • Maximilian Oligschläger: Injury Diagnostics in the Human Knee based on a Continuum Modeling Approach and Bayesian Inverse Analysis, Bachelor's Thesis (completed 10.2021, supervised together with Renate Sachse)
  • Maximilian Dinkel: A Bayesian inference approach for optimal tumor treatment using Gaussian Processes, Term Paper (completed 06.2021, supervised together with Barbara Wirthl)
  • Gil Robalo Rei: Development of an Accurate and Efficient Gradient-Free Variational Inference Algorithm for the Solution of Complex Bayesian Inverse Problems, Master's Thesis (completed 03.2021)
  • Jakob Huber: Towards uncertainty quantification in all solid-state batteries, Master's Thesis (completed 01.2021, supervised together with Christoph Schmidt)
  • Tobias Wanninger: High-Dimensional Uncertainty Quantification in Low Mach Number Flows Using a Bayesian Multi-Fidelity Approach, Term Paper (completed 12.2020, supervised together with Dr.-Ing. Volker Gravemeier)
  • Dennis Berninger: Towards an efficient multi-fidelity approach for variance based global sensitivity analysis with application to large-scale vibroacoustic vehicle models, external Master's Thesis at BMW (completed 03.2021, together with Rupert Ullmann)
  • Fong-Lin Wu: High-dimensional uncertainty quantification for solution fields using a Bayesian multi-fidelity (BMFMC) and a partial least-squares polynomial chaos expansion (PLS-PCE) approach, Master's Thesis (completed 09.2020, supervised together with Max Ehre)
  • Catrin Rodenberg: Global Sensitivity Analysis of a Multiphase Model for Avascular Tumor Growth, Master's Thesis (completed 07.2020, supervised together with Johannes Kremheller)
  • Gil Robalo Rei: Towards Uncertainty Quantification in Fluid-Structure Interaction using Bayesian Multi-Fidelity Monte Carlo Methods, Term Paper (completed 05.2019)

Education

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