Nikolaj Mücke

Volledige naam

N.T. Mücke

E-mail

Nikolaj.Mucke@cwi.nl

Telefoon

+31 20 592 4247

Kamer

L141

Afdeling(en)

Scientific Computing

Functie(s)

PhD student

Startpagina

https://www.nikolajmucke.com/

Biography

Nikolaj Mücke works a PhD student in the Scientific Computing Group at CWI. His work centers around combining methods and approaches from the areas of numerical analysis and machine learning in order to solve complicated partial differential equation problems. Currently, the focus is on effective and robust reduced order modelling for time-dependent fluid problems. Prior to working as a PhD student he received his B.Sc. and M.Sc. from the Technical University of Denmark (DTU).

Mücke, N. (2024, June 27). Deep learning for real-time inverse problems and data assimilation with uncertainty quantification for digital twins.
Mücke, N., Sanderse, B., Bohte, S., & Oosterlee, K. (2023). Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications. Computers & Mathematics with Applications, 147, 278–299. doi:10.1016/j.camwa.2023.07.028
Mücke, N., Pandey, P., Jain, S., Bohte, S., & Oosterlee, K. (2023). A probabilistic digital twin for leak localization in water distribution networks using generative deep learning. Sensors, 23(13). doi:10.3390/s23136179
Mücke, N., Bohte, S., & Oosterlee, K. (2021). Reduced order modeling for parameterized time-dependent PDEs using spatially and memory aware deep learning. Journal of Computational Science, 53. doi:10.1016/j.jocs.2021.101408

Nikolaj Mücke

Biography

Publications

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