This image showsStefania Scheurer

Stefania Scheurer

M.Sc.

PhD Student
Institute for Modelling Hydraulic and Environmental Systems
Dept. of Stochastic Simulation and Safety Research for Hydrosystems, SimTech

Contact

+49 711 685 60710

Pfaffenwaldring 5a
70569 Stuttgart
Room: 2.33

  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. 2026

    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. 2026 May;8.
    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, 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.
    4. 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.
    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. Pawusch L, Scheurer S, Nowak W, Maxwell R. HydroStartML: A combined machine learning and physics-based approach to reduce hydrological model spin-up time. Advances in Water Resources. 2025 Sep;206:105124.
    3. Pawusch L, Scheurer S, Nowak W, Maxwell R. Development of a Combined Machine Learning and Physics-based Approach to Reduce Hydrologic Model Spin-up Time. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2025; 2025.

  4. 2024

    1. Bartsch J, Knopf P, Scheurer S, Weber J. Controlling a Vlasov-Poisson Plasma by a Particle-in-Cell Method based on a Monte Carlo Framework. SIAM Journal on Control and Optimization. 2024 Jul;62:1977–2011.
    2. Scheurer S, Nowak W. Neural Process Regression. In: geoENV2024 Book of Abstracts. Chania, Crete, GR: Creative Commons Licence BY-NC-ND 4.0; 2024. pp. 155–6.

  5. 2023

    1. Wildt N, Scheurer S, Nowak W, Haslauer C. Learning PFAS mechanisms with a FInite Volume Neural Network (FINN). In: Fall Meeting 2023. San Francisco, CA, USA: American Geophysical Union (AGU); 2023.

  6. 2021

    1. Scheurer S, Schäfer Rodrigues Silva A, Mohammadi F, Hommel J, Oladyshkin S, Flemisch B, et al. Surrogate-based Bayesian Comparison of Computationally Expensive Models: Application to Microbially Induced Calcite Precipitation. Computational Geosciences. 2021;25:1899–917.

11/2019 B.Sc. Simulation Technology, University of Stuttgart
10/2022 M.Sc. Simulation Technology, University of Stuttgart
Since 01/2023 PhD Student, Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart

Uncertainty quantification for and with machine learning

Project: Bayesian Geostatistics for Non-Stationary and Non-Gaussian Random Fields with SimTech

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