4 papers
SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms
Maochao Xiao, Yuning Wang, Felix Rodach +15
Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and disco…
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…
Invariant Control Strategies for Active Flow Control using Graph Neural Networks
Marius Kurz, Rohan Kaushik, Marcel Blind +4
Reinforcement learning has gained traction for active flow control tasks, with initial applications exploring drag mitigation via flow field augmentation around a two-dimensional c…
GALÃXI: Solving complex compressible flows with high-order discontinuous Galerkin methods on accelerator-based systems
Daniel Kempf, Marius Kurz, Marcel Blind +6
This work presents GALAEXI as a novel, energy-efficient flow solver for the simulation of compressible flows on unstructured meshes leveraging the parallel computing power of moder…