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Nikolaj Takata Mücke
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Year
Reduced order modeling for parameterized time-dependent PDEs using spatially and memory aware deep learning
NT Mücke, SM Bohté, CW Oosterlee
Journal of Computational Science 53, 101408, 2021
452021
Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications
NT Mücke, B Sanderse, SM Bohté, CW Oosterlee
Computers & Mathematics with Applications 147, 278-299, 2023
72023
Reduced Order Modeling for Nonlinear PDE-constrained Optimization using Neural Networks
NT Mücke, LH Christiansen, AP Karup-Engsig, JB Jørgensen
2019 IEEE 58th Conference on Decision and Control (CDC), 2019
62019
A probabilistic digital twin for leak localization in water distribution networks using generative deep learning
NT Mücke, P Pandey, S Jain, SM Bohté, CW Oosterlee
Sensors 23 (13), 6179, 2023
52023
AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage
GS Seabra, NT Mücke, VLS Silva, D Voskov, FC Vossepoel
International Journal of Greenhouse Gas Control 136, 104190, 2024
42024
Reduced Order Modelling for Dispersive and Nonlinear Water Wave Modelling
FG Eroglu, NT Mücke, AP Engsig-Karup
37th International Workshop on Water Waves and Floating Bodies, 2022
22022
Advancing Data Assimilation and Uncertainty Quantification for CO2 Sequestration through AI-Hybrid Methods
GS Seabra, NT Mücke, VLS Silva, D Voskov, F Vossepoel
ECMOR 2024 2024 (1), 1-23, 2024
2024
The deep latent space particle filter for real-time data assimilation with uncertainty quantification
NT Mücke, SM Bohté, CW Oosterlee
Scientific Reports 14 (1), 19447, 2024
2024
Deep Learning for Real-Time Inverse Problems and Data Assimilation with Uncertainty Quantification for Digital Twins
NT Mücke
Utrecht University, 2024
2024
Reduced Order Modelling for Wave-Structure Modelling
FG Eroglu, NT Mücke, J Visbech, AP Engsig-Karup
22nd IACM Computational Fluids Conference, 2022
2022
Why use Deep Neural Networks?
NT Mücke
2019
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Articles 1–11