This image shows Nils Wildt

Nils Wildt

M.Sc.

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

Contact

Pfaffenwaldring 5a
70569 Stuttgart
Room: 2.33

  1. 2024 (submitted)

    1. Ejaz F, Wildt N, Wöhling T, Nowak W. Estimating total groundwater storage and its associated uncertainty through spatiotemporal Kriging of groundwater-level data. Journal of Hydrology.
    2. Xu T, Xiao S, Reuschen S, Wildt N, Franssen HJH, Nowak W. Towards a community-wide effort for benchmarking in subsurface hydrological inversion:                              benchmarking cases, high-fidelity reference solutions, procedure and a first comparison. Hydrology and Earth System Sciences.
    3. Kröker I, Brünnette T, Wildt N, Oreamuno MFM, Kohlhaas R, Oladyshkin S, et al. Bayesian Active Learning for Regularized Multi-Resolution Arbitrary Polynomial Chaos using Information Theory. International Journal for Uncertainty Quantification.
  2. 2024

    1. Ejaz F, Wildt N, Wöhling T, Nowak W. Estimating catchment-wide total groundwater storage via space-time kriging provides calibration data for catchment-scale groundwater balance models. In: Geophys Res Abstr. Vienna: EGU General Assembly 2024; 2024. (Geophys. Res. Abstr.; vols. 25, EGU2024-10521).
  3. 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. (Fall Meeting 2023).

09/2017 B.Sc. Simulation Technology, University of Stuttgart
04/2020 M.Sc. Simulation Technology, University of Stuttgart
09/2020-01/2022 Scientific Assistant, Institute for Systems Theory and Automatic Control (IST), University of Stuttgart
Since 02/2022 PhD Student, Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart

Regression learning
Bayesian statistics and calibration
Diffusion-Reaction modeling and simulation

Project: Learning Mechanisms of Phenomena from Observations

To the top of the page