3 papers
physics.flu-dyn2025
Generative Super-Resolution of Turbulent Flows via Stochastic Interpolants
Martin Schiødt, Nikolaj Takata Mücke, Clara Marika Velte
Capturing the intricate multiscale features of turbulent flows remains a fundamental challenge due to the limited resolution of experimental data and the computational cost of high…
cs.CE2025
Physics-aware generative models for turbulent fluid flows through energy-consistent stochastic interpolants
Nikolaj T. Mücke, Benjamin Sanderse
Generative models have demonstrated remarkable success in domains such as text, image, and video synthesis. In this work, we explore the application of generative models to fluid d…
cs.CE2024
The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification
Nikolaj T. Mücke, Sander M. Bohté, Cornelis W. Oosterlee
In Data Assimilation, observations are fused with simulations to obtain an accurate estimate of the state and parameters for a given physical system. Combining data with a model, h…