Nikolaj Mücke

Full Name

N.T. Mücke

Nikolaj.Mucke@cwi.nl

Phone

+31 20 592 4247

Room

L141

Department(s)

Scientific Computing

Function(s)

Researcher

Homepage

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., Bohte, S., & Oosterlee, K. (2024). The deep latent space particle filter for real-time data assimilation with uncertainty quantification. Nature Scientific Reports, 14, 19447:1–19447:17. doi:10.1038/s41598-024-69901-7
Pandey, P., Mücke, N., Jain, S., Ramachandran, P., Bohte, S., & Oosterlee, K. (2024). Machine learning-based digital twin for water distribution network anomaly detection and localization. In Proceedings of the International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry. doi:10.3390/engproc2024069201
Seabra, G. S., Mücke, N., Silva, V. L. S., Voskov, D., & Vossepoel, F. (2024). AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage. International Journal of Greenhouse Gas Control, 136, 104190:1–104190:22. doi:10.1016/j.ijggc.2024.104190
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

Entropy-consistent learning: harnessing the power of generative AI for realistic physics simulations (None)

Nikolaj Mücke

Biography

Publications

Current projects with external funding

Nikolaj Mücke

Biography

Publications

Current projects with external funding

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