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Ricardo Vinuesa
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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
24652020
Enhancing computational fluid dynamics with machine learning
R Vinuesa, SL Brunton
Nature Computational Science 2 (6), 358-366, 2022
4192022
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
3132022
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
2652019
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
2132021
History effects and near equilibrium in adverse-pressure-gradient turbulent boundary layers
A Bobke, R Vinuesa, R Örlü, P Schlatter
Journal of Fluid Mechanics 820, 667-692, 2017
1962017
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
1642018
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
1622016
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
1522014
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
1502021
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
1432016
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
1382021
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
1352021
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
1262022
Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions
C Vignon, J Rabault, R Vinuesa
Physics of fluids 35 (3), 2023
1172023
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
1142013
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
1102016
Improving aircraft performance using machine learning: A review
S Le Clainche, E Ferrer, S Gibson, E Cross, A Parente, R Vinuesa
Aerospace Science and Technology 138, 108354, 2023
1082023
Interpretable deep-learning models to help achieve the Sustainable Development Goals
R Vinuesa, B Sirmacek
Nature Machine Intelligence 3 (11), 926-926, 2021
1072021
Assessing whether artificial intelligence is an enabler or an inhibitor of sustainability at indicator level
S Gupta, SD Langhans, S Domisch, F Fuso-Nerini, A Felländer, ...
Transportation Engineering 4, 100064, 2021
992021
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