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.
The Deep Ritz Method: A Deep Learning-Based Numerical Algorithm for Solving Variational Problems.Communications in Mathematics and Statistics, 6(1):1–12, February 2018
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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.