Dieses Bild zeigtNils 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

    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.
    2. Wildt N, Tartakovsky DM, Oladyshkin S, Nowak W. CODE: a global approach to ODE learning. Journal of Machine Learning for Modeling and Computing. 2026 May;7:73–105.

  2. 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.

  3. 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.

  4. 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.

  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.

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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