Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence
This work addresses turbulence prediction for fluid dynamics researchers, offering incremental improvements in computational efficiency and accuracy.
The study tackled predicting temporal dynamics in a low-order turbulence model, finding that LSTM networks achieve excellent long-term statistical accuracy with errors below 1%, and a new Koopman-based method (KNF) matches this accuracy at lower computational cost while outperforming LSTM in short-term predictions.
The capabilities of recurrent neural networks and Koopman-based frameworks are assessed in the prediction of temporal dynamics of the low-order model of near-wall turbulence by Moehlis et al. (New J. Phys. 6, 56, 2004). Our results show that it is possible to obtain excellent reproductions of the long-term statistics and the dynamic behavior of the chaotic system with properly trained long-short-term memory (LSTM) networks, leading to relative errors in the mean and the fluctuations below $1\%$. Besides, a newly developed Koopman-based framework, called Koopman with nonlinear forcing (KNF), leads to the same level of accuracy in the statistics at a significantly lower computational expense. Furthermore, the KNF framework outperforms the LSTM network when it comes to short-term predictions. 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. Thus, we propose a model-selection criterion based on the computed statistics which allows to achieve excellent statistical reconstruction even on small datasets, with minimal loss of accuracy in the instantaneous predictions.