22 citations · 35 across the 3 of their papers we have counts for
6 papers
Predicting the near-wall region of turbulence through convolutional neural networks
A. G. Balasubramanian, L. Guastoni, A. Güemes +5
Modelling the near-wall region of wall-bounded turbulent flows is a widespread practice to reduce the computational cost of large-eddy simulations (LESs) at high Reynolds number. A…
Convolutional-network models to predict wall-bounded turbulence from wall quantities
L. Guastoni, A. Güemes, A. Ianiro +4
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 chan…
Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence
Hamidreza Eivazi, Luca Guastoni, Philipp Schlatter +2
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 Moe…
On the use of recurrent neural networks for predictions of turbulent flows
Luca Guastoni, Prem A. Srinivasan, Hossein Azizpour +2
In this paper, the prediction capabilities of recurrent neural networks are assessed in the low-order model of near-wall turbulence by Moehlis {\it et al.} (New J. Phys. {\bf 6}, 5…
Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks
L. Guastoni, M. P. Encinar, P. Schlatter +2
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-sh…
Predictions of turbulent shear flows using deep neural networks
P. A. Srinivasan, L. Guastoni, H. Azizpour +2
In the present work we assess the capabilities of neural networks to predict temporally evolving turbulent flows. In particular, we use the nine-equation shear flow model by Moehli…