This image shows Stefania 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

    1. 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.
    2. 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.

  2. 2025 (submitted)

    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. 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.

  3. 2025

    1. 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.
    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. Scheurer S, Frenner R, Brünnette T, Nowak W. Efficient ML-Assisted Backward Uncertainty Quantification for a Physics-Aware ML Model. Gothenburg, SWE; 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, Causal, Universal Differential Equation Learner with SimTech

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