<ul><li>This work proposes a novel methodology for turbulence modeling in Large Eddy Simulation (LES) based on Graph Neural Networks (GNNs).</li><li>The proposed approach embeds the symmetries of the Navier-Stokes equations into the model architecture, resulting in a symmetry-preserving simulation setup.</li><li>The GNN models are trained successfully in actual simulations using Reinforcement Learning (RL), ensuring consistency with the underlying LES formulation and discretization.</li><li>The GNN model demonstrates the potential for turbulence modeling, particularly in the context of LES and RL.</li></ul>

Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks

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