Recurrently training neural operators on their own predictions reduces long-term forecast error and error growth compared with teacher forcing, though the theoretical linear-growth proof depends on an unproven assumption.
Approximating Numerical Fluxes Using Fourier Neural Operators for Hyperbolic Conservation Laws.Communications in Computational Physics, 37(2):420–456, January 2025
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Recurrent Neural Operators: Stable Long-Term PDE Prediction
Recurrently training neural operators on their own predictions reduces long-term forecast error and error growth compared with teacher forcing, though the theoretical linear-growth proof depends on an unproven assumption.