Ricardo Vinuesa
Zitiert von
Zitiert von
The role of artificial intelligence in achieving the Sustainable Development Goals
R Vinuesa, H Azizpour, I Leite, M Balaam, V Dignum, S Domisch, ...
Nature communications 11 (1), 1-10, 2020
Enhancing computational fluid dynamics with machine learning
R Vinuesa, SL Brunton
Nature Computational Science 2 (6), 358-366, 2022
Predictions of turbulent shear flows using deep neural networks
PA Srinivasan, L Guastoni, H Azizpour, P Schlatter, R Vinuesa
Physical Review Fluids 4 (5), 054603, 2019
Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations
H Eivazi, M Tahani, P Schlatter, R Vinuesa
Physics of Fluids 34 (7), 2022
Convolutional-network models to predict wall-bounded turbulence from wall quantities
L Guastoni, A Güemes, A Ianiro, S Discetti, P Schlatter, H Azizpour, ...
Journal of Fluid Mechanics 928, A27, 2021
History effects and near equilibrium in adverse-pressure-gradient turbulent boundary layers
A Bobke, R Vinuesa, R Örlü, P Schlatter
J. Fluid Mech 820, 667-692, 2017
Aspect ratio effects in turbulent duct flows studied through direct numerical simulation
R Vinuesa, A Noorani, A Lozano-Durán, GKE Khoury, P Schlatter, ...
Journal of Turbulence 15 (10), 677-706, 2014
Turbulent boundary layers around wing sections up to Rec= 1,000,000
R Vinuesa, PS Negi, M Atzori, A Hanifi, DS Henningson, P Schlatter
International Journal of Heat and Fluid Flow 72, 86-99, 2018
Direct numerical simulation of the flow around a wing section at moderate Reynolds number
SM Hosseini, R Vinuesa, P Schlatter, A Hanifi, DS Henningson
International Journal of Heat and Fluid Flow 61, 117-128, 2016
COVID-19 digital contact tracing applications and techniques: A review post initial deployments
M Shahroz, F Ahmad, MS Younis, N Ahmad, MNK Boulos, R Vinuesa, ...
Transportation Engineering 5, 100072, 2021
On determining characteristic length scales in pressure-gradient turbulent boundary layers
R Vinuesa, A Bobke, R Örlü, P Schlatter
Physics of fluids 28 (5), 2016
An interpretable framework of data-driven turbulence modeling using deep neural networks
C Jiang, R Vinuesa, R Chen, J Mi, S Laima, H Li
Physics of Fluids 33 (5), 2021
From coarse wall measurements to turbulent velocity fields through deep learning
A Güemes, S Discetti, A Ianiro, B Sirmacek, H Azizpour, R Vinuesa
Physics of fluids 33 (7), 2021
Obtaining accurate mean velocity measurements in high Reynolds number turbulent boundary layers using Pitot tubes
SCC Bailey, M Hultmark, JP Monty, PH Alfredsson, MS Chong, ...
Journal of Fluid Mechanics 715, 642-670, 2013
Secondary flow in turbulent ducts with increasing aspect ratio
R Vinuesa, P Schlatter, HM Nagib
Physical Review Fluids 3 (5), 054606, 2018
Convergence of numerical simulations of turbulent wall-bounded flows and mean cross-flow structure of rectangular ducts
R Vinuesa, C Prus, P Schlatter, HM Nagib
Meccanica 51, 3025-3042, 2016
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
H Eivazi, S Le Clainche, S Hoyas, R Vinuesa
Expert Systems with Applications 202, 117038, 2022
Interpretable deep-learning models to help achieve the Sustainable Development Goals
R Vinuesa, B Sirmacek
Nature Machine Intelligence 3 (11), 926-926, 2021
Recurrent neural networks and Koopman-based frameworks for temporal predictions in turbulence
H Eivazi, L Guastoni, P Schlatter, H Azizpour, R Vinuesa
International Journal of Heat and Fluid Flow 90, 108816, 2021
Direct numerical simulation of the flow around a wall-mounted square cylinder under various inflow conditions
R Vinuesa, P Schlatter, J Malm, C Mavriplis, DS Henningson
Journal of Turbulence 16 (6), 555-587, 2015
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