A self-supervised framework that discovers governing equations from short observed data windows and uses them to regularize autoregressive PDE foundation models, improving long-term forecast accuracy.
The total length of the testing dataset consists of 20 steps
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Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models
A self-supervised framework that discovers governing equations from short observed data windows and uses them to regularize autoregressive PDE foundation models, improving long-term forecast accuracy.