| 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 | 2472 | 2020 |
| Enhancing computational fluid dynamics with machine learning R Vinuesa, SL Brunton Nature Computational Science 2 (6), 358-366, 2022 | 415 | 2022 |
| 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 | 312 | 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 | 263 | 2019 |
| 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 | 212 | 2021 |
| 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 | 189 | 2017 |
| 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 | 165 | 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 | 163 | 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 | 150 | 2021 |
| 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 | 150 | 2014 |
| 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 | 143 | 2016 |
| 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 | 138 | 2021 |
| 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 | 136 | 2021 |
| 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 | 121 | 2022 |
| 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 | 114 | 2013 |
| 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 | 112 | 2023 |
| 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 | 107 | 2016 |
| Interpretable deep-learning models to help achieve the Sustainable Development Goals R Vinuesa, B Sirmacek Nature Machine Intelligence 3 (11), 926-926, 2021 | 106 | 2021 |
| 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 | 104 | 2023 |
| 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 | 96 | 2021 |
Hossein AzizpourAssociate Professor, KTH (Royal Institute of Technology)kth.se의 이메일 확인됨