4 citations · 8 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Scientific machine learning for closure models in multiscale problems: a review
Benjamin Sanderse, Panos Stinis, Romit Maulik +1
Closure problems are omnipresent when simulating multiscale systems, where some quantities and processes cannot be fully prescribed despite their effects on the simulation's accura…
Energy-Conserving Neural Network for Turbulence Closure Modeling
Toby van Gastelen, Wouter Edeling, Benjamin Sanderse
In turbulence modeling, we are concerned with finding closure models that represent the effect of the subgrid scales on the resolved scales. Recent approaches gravitate towards mac…