Replay training on the DNN-MG's own perturbed trajectories removes the long-time instability of the hybrid Navier-Stokes solver, while Transformers or larger patches improve accuracy on unseen geometries.
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A robust and stable hybrid neural network/finite element method for 2D flows that generalizes to different geometries
Replay training on the DNN-MG's own perturbed trajectories removes the long-time instability of the hybrid Navier-Stokes solver, while Transformers or larger patches improve accuracy on unseen geometries.