A graph-neural-network autoencoder combined with a transformer predicts 2D cylinder and backward-facing-step flows on unstructured meshes, matching OpenFOAM fields while running about 100 to 900 times faster.
Theproperorthogonaldecompositioninthe analysis of turbulent flows.Annual review of fluid mechanics, 25(1):539–575, 1993
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Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows
A graph-neural-network autoencoder combined with a transformer predicts 2D cylinder and backward-facing-step flows on unstructured meshes, matching OpenFOAM fields while running about 100 to 900 times faster.