Dieses Bild zeigt Nils Wildt

Nils Wildt

Herr M.Sc.

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

Kontakt

+49 711 685 60109

Pfaffenwaldring 5a
70569 Stuttgart
Raum: 2.33

  1. 2026 (accepted)

    1. Wildt N, Tartakovsky DM, Oladyshkin S, Nowak W. CODE: a global approach to ODE learning. Journal of Machine Learning for Modeling and Computing.

  2. 2026

    1. Wildt N, Callau Medrano S, Riazi M, Oladyshkin S, Nowak W. Deep arbitrary Polynomial Chaos Expansion ODE for Hydrological System Modeling: It is all about that Basis. Bologna, Italy: 26th International Conference on Computational Methods in Water Resources (CMWR); 2026.

  3. 2025 (submitted)

    1. Ejaz F, Wildt N, Wöhling T, Nowak W. Estimating groundwater levels and their associated uncertainty through spatiotemporal Kriging of groundwater-level data. Hydrogeology Journal.

  4. 2025

    1. 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.

  5. 2024

    1. Xu T, Xiao S, Reuschen S, Wildt N, Franssen H-JH, 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 [Internet]. 2024;28:5375–400. Available from: https://hess.copernicus.org/articles/28/5375/2024/
    2. Wildt N, Oladyshkin S. Learning Kinetic Sorption Mechanisms Using Ordinary Differential Arbitrary Polynomial Chaos Expansion. Tucson, Arizona, USA: 25th International Conference on Computational Methods in Water Resources (CMWR); 2024.
    3. 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.

  6. 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.

09/2017 B.Sc. Simulation Technology, Universität Stuttgart
04/2020 M.Sc. Simulation Technology, Universität Stuttgart
09/2020-01/2022 Wissenschaftlicher Mitarbeiter, Institut für Systemtheorie und Regelungstechnik (IST), Universität Stuttgart
Seit 02/2022 Doktorand, Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart

 

Regressionslernen
Bayessche Statistik und Kalibrierung
Diffusions-Reaktions-Modellierung und Simulation

Projekt: Lernen physikalischer Zusammenhänge anhand von Messdaten

Code: https://github.com/NilsWildt

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