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Found 4 papers, 0 papers with code
Easy attention: A simple self-attention mechanism for transformer-based time-series reconstruction and prediction
To improve the robustness of transformer neural networks used for temporal-dynamics prediction of chaotic systems, we propose a novel attention mechanism called easy attention which we demonstrate in time-series reconstruction and prediction.
Predicting the temporal dynamics of turbulent channels through deep learning
Alternative reduced-order models (ROMs), based on the identification of different turbulent structures, are explored and they continue to show a good potential in predicting the temporal dynamics of the minimal channel.
Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence
We also observe that using a loss function based only on the instantaneous predictions of the chaotic system can lead to suboptimal reproductions in terms of long-term statistics.
On the use of recurrent neural networks for predictions of turbulent flows
We also observe that using a loss function based only on the instantaneous predictions of the flow may not lead to the best predictions in terms of turbulence statistics, and it is necessary to define a stopping criterion based on the computed statistics.
