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Found 4 papers, 1 papers with code
Predicting the near-wall region of turbulence through convolutional neural networks
At $Re_{\tau}=550$, the FCN 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 less than 20% error in prediction of streamwise-fluctuations intensity.
Convolutional-network models to predict wall-bounded turbulence from wall quantities
Two models based on convolutional neural networks are trained to predict the two-dimensional velocity-fluctuation fields at different wall-normal locations in a turbulent open channel flow, using the wall-shear-stress components and the wall pressure as inputs.
Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks
A fully-convolutional neural-network model is used to predict the streamwise velocity fields at several wall-normal locations by taking as input the streamwise and spanwise wall-shear-stress planes in a turbulent open channel flow.
Predictions of turbulent shear flows using deep neural networks
In the present work we assess the capabilities of neural networks to predict temporally evolving turbulent flows.
Fluid Dynamics Computational Physics
