Dieses Bild zeigtTim Brünnette

Tim Brünnette

Herr M.Sc.

Doktorand
Institut für Wasser- und Umweltsystemmodellierung
Lehrstuhl für Stochastische Simulation und Sicherheitsforschung für Hydrosysteme, SimTech

Kontakt

+49 711 685 60874

Pfaffenwaldring 5a
70569 Stuttgart
Raum: 2.35

  1. 2026 (submitted)

    1. Scheurer S, Scholze P, Brünnette T, Krueger J, Nowak W, Tartakovsky DM. Error Bounds on Effective Diffusion Tensors for Imaged Porous Media. Journal of Computational Physics.
    2. Brünnette T, Kaiserauer A, Stegmeyer T, Juwailes Y, Wolf V, Nowak W. Estimating airplane debris locations with Bayesian inversion. PLOS ONE.

  2. 2026

    1. Scheurer S, Reiser P, Brünnette T, Nowak W, Guthke A, Bürkner P-C. UA-SABI: Uncertainty-aware surrogate-based amortized Bayesian inference for computationally expensive subsurface flow and transport models. Bologna, Italy: 26th International Conference on Computational Methods in Water Resources (CMWR); 2026.
    2. Scheurer S, Reiser P, Brünnette T, Nowak W, Guthke A, Bürkner P-C. Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models. Transactions in Machine Learning Research [Internet]. 2026 Jan; Available from: https://openreview.net/pdf?id=aVSoQXbfy1
    3. Scheurer S, Frenner R, Brünnette T, Oladyshkin S, Nowak W. Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion–Sorption Models. Frontiers in Water: Advances in Model-Data Fusion for Water Resources Problems. 2026 May;8.
    4. Brünnette T, Hörl M, Kohlhaas R, Oukili H. Bayesian Model Validation for Single-Phase Darcy Flow in Fractured Porous Media. Bologna, Italy: 26th International Conference on Computational Methods in Water Resources (CMWR); 2026.
    5. Scheurer S, Frenner R, Brünnette T, Oladyshkin S, Nowak W. Efficient Uncertainty Quantification for Physics-Aware Machine Learning of Diffusion-Sorption Models. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2026; 2026.
    6. Morales Oreamuno MF, Brünnette T, Scheurer S, Oladyshkin S, Nowak W. Information-Theoretic Bayesian Active Learning for Surrogate Training and Inverse Modeling in Subsurface Transport Applications. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2026; 2026.

  3. 2025

    1. Scheurer S, Frenner R, Brünnette T, Nowak W. Efficient ML-Assisted Backward Uncertainty Quantification for a Physics-Aware ML Model. Gothenburg, SWE; 2025.
    2. Kröker I, Brünnette T, Wildt N, Oreamuno MFM, Kohlhaas R, Oladyshkin S, et al. Bayesian3 Active Learning for Regularized Multi-Resolution Arbitrary Polynomial Chaos using Information Theory. International Journal for Uncertainty Quantification. 2025 Jan;15:21–54.
    3. Brünnette T, Kaiserauer A, Nowak W. Localization of missing debris pieces after aircraft crashes - Stochastic simulation and inference. Gothenburg, SWE; 2025.

  4. 2024

    1. Nowak W, Brünnette T, Schalkers MA, Möller M. Overdispersion in gate tomography: Experiments and continuous, two-scale random walk model on the Bloch sphere. ACM Transactions on Quantum Computing [Internet]. 2024 Oct;5:1–17. Available from: https://doi.org/10.1145/3688857
    2. Bruennette T, Nowak W. Efficient Inference for Non-Deterministic Fractures. In: geoENV2024 Book of Abstracts. Chania, Crete, GR: Creative Commons Licence BY-NC-ND 4.0; 2024. pp. 67–8.

  5. 2023

    1. Bruennette T, Werneck L, Keip M-A, Nowak W. Random Fracture Models - Towards Statistical Realism and Validation. In: Fall Meeting 2023. San Francisco, CA, USA: American Geophysical Union (AGU); 2023.
    2. Hermann F, Michalowski A, Brünnette T, Reimann P, Vogt S, Graf T. Data-Driven Prediction and Uncertainty Quantification of Process Parameters for Directed Energy Deposition. Materials [Internet]. 2023 Nov;16. Available from: https://www.mdpi.com/1996-1944/16/23/7308

  6. 2019

    1. Brünnette T, Santin G, Haasdonk B. Greedy Kernel Methods for Accelerating Implicit Integrators for Parametric ODEs. In: Numerical Mathematics and Advanced Applications - ENUMATH 2017. 2019. pp. 889–96.

08/2017 B.Sc. Simulation Technology, Universität Stuttgart
11/2021 Doppeldiplom M.Sc. Simulation Technology, Universität Stuttgart & M.Sc. Industrielle und Angewandte Mathematik, TU Eindhoven, Niederlande
Seit 02/2022 Doktorand, Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart

Frakturnetzwerke in Wassersystemen
Stochastische Prozesse

Projekt: Randomisieren von Modellen für rissdurchsetzte poröse Medien: Auswirkungen auf Druck, Diffusion und Transport mit SFB1313

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