Adding graph-Laplacian band-power matching to the training loss improves long-horizon autoregressive forecasting of chaotic flows on unstructured meshes.
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Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses
Adding graph-Laplacian band-power matching to the training loss improves long-horizon autoregressive forecasting of chaotic flows on unstructured meshes.