Reduced Order Modeling for Nonlinear PDE-constrained Optimization using Neural Networks. - Nikolaj Takata Mücke; Lasse Hjuler Christiansen; Allan Peter Engsig-Karup; John Bagterp Jørgensen (CDC, 2019)

Reduced order modeling for parameterized time-dependent PDEs using spatially and memory aware deep learning. - Nikolaj Takata Mücke; Sander M. Bohté; Cornelis W. Oosterlee (J. Comput. Sci., 2021)

A Probabilistic Digital Twin for Leak Localization in Water Distribution Networks Using Generative Deep Learning. - Nikolaj Takata Mücke; Prerna Pandey; Shashi Jain; Sander M. Bohté; Cornelis W. Oosterlee (Sensors, 2023)

Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications. - Nikolaj Takata Mücke; Benjamin Sanderse; Sander M. Bohté; Cornelis W. Oosterlee (Comput. Math. Appl., 2023)

The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification. - Nikolaj Takata Mücke; Sander M. Bohté; Cornelis W. Oosterlee (CoRR, 2024)

Deep Learning for Real-Time Inverse Problems and Data Assimilation with Uncertainty Quantification for Digital Twins. - Nikolaj Takata Mücke (2024)

Physics-aware generative models for turbulent fluid flows through energy-consistent stochastic interpolants. - Nikolaj Takata Mücke; Benjamin Sanderse (CoRR, 2025)