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…
math.NA2020
Reduced Order Modeling for Parameterized Time-Dependent PDEs using Spatially and Memory Aware Deep Learning
Nikolaj T. Mücke, Sander M. Bohté, Cornelis W. Oosterlee
We present a novel reduced order model (ROM) approach for parameterized time-dependent PDEs based on modern learning. The ROM is suitable for multi-query problems and is nonintrusi…