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Predicting the wall-shear stress and wall pressure through convolutional neural networks
At $Re_{\tau}=550$, both FCN and R-Net can take advantage of the self-similarity in the logarithmic region of the flow and predict the velocity-fluctuation fields at $y^{+} = 50$ using the velocity-fluctuation fields at $y^{+} = 100$ as input with about 10% error in prediction of streamwise-fluctuations intensity.
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.
Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations
We first show the applicability of PINNs for solving the Navier-Stokes equations for laminar flows by solving the Falkner-Skan boundary layer.
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.
