3 papers
cs.LG2025
Energy-Conserving Neural Network Closure Model for Long-Time Accurate and Stable LES
Toby van Gastelen, Wouter Edeling, Benjamin Sanderse
Machine learning-based closure models for LES have shown promise in capturing complex turbulence dynamics but often suffer from instabilities and physical inconsistencies. In this…
math.NA2025
Modeling Advection-Dominated Flows with Space-Local Reduced-Order Models
Toby van Gastelen, Wouter Edeling, Benjamin Sanderse
Reduced-order models (ROMs) are often used to accelerate the simulation of large physical systems. However, traditional ROM techniques, such as those based on proper orthogonal dec…
physics.flu-dyn2025
Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks
Marius Kurz, Andrea Beck, Benjamin Sanderse
This work proposes a novel methodology for turbulence modeling in Large Eddy Simulation (LES) based on Graph Neural Networks (GNNs), which embeds the discrete rotational, reflectio…