5 citations · 6 across the 2 of their papers we have counts for
2 papers
physics.flu-dyn2022★ 1 cited
Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers
L. Guastoni, A. G. Balasubramanian, F. Foroozan +6
Fully-convolutional neural networks (FCN) were proven to be effective for predicting the instantaneous state of a fully-developed turbulent flow at different wall-normal locations…
physics.flu-dyn2022★ 5 cited
Super-resolution GANs of randomly-seeded fields
Alejandro Güemes, Carlos Sanmiguel Vila, Stefano Discetti
Reconstruction of field quantities from sparse measurements is a problem arising in a broad spectrum of applications. This task is particularly challenging when the mapping between…