Dieses Bild zeigt Tim 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 (accepted)

    1. 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.

  2. 2026

    1. 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
    2. 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.
    3. 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.
    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. 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

Teamseite
Zum Seitenanfang