56 citations · 95 across the 6 of their papers we have counts for
11 papers
An Uncertainty-Quantification Framework for Assessing Accuracy, Sensitivity, and Robustness in Computational Fluid Dynamics
Saleh Rezaeiravesh, Ricardo Vinuesa, Philipp Schlatter
A framework is developed based on different uncertainty quantification (UQ) techniques in order to assess validation and verification (V&V) metrics in computational physics problem…
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…
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…
Coherent structures and secondary flow in turbulent square duct
Marco Atzori, Ricardo Vinuesa, Adrián Lozano-Durán +1
The aim of the present work is to investigate the role of coherent structures in the generation of the secondary flow in a turbulent square duct. The coherent structures are define…
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…