51 citations · 80 across the 3 of their papers we have counts for
4 papers · 1 filter
Machine learning flow control with few sensor feedback and measurement noise
R. Castellanos, G. Y. Cornejo Maceda, I. de la Fuente +3
A comparative assessment of machine learning (ML) methods for active flow control is performed. The chosen benchmark problem is the drag reduction of a two-dimensional Kármán vorte…
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…
From coarse wall measurements to turbulent velocity fields through deep learning
Alejandro Güemes, Stefano Discetti, Andrea Ianiro +3
This work evaluates the applicability of super-resolution generative adversarial networks (SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse…
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…