Ricardo Vinuesa

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	Hepsi	2020 yılından bugüne
Alıntılar	10660	9658
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Finansman sağlayan kuruluşun getirdiği zorunluluğa dayalı olarak

Katkıda bulunan yazarlar

Hossein AzizpourAssociate Professor, KTH (Royal Institute of Technology)kth.se üzerinde doğrulanmış e-posta adresine sahip
Hassan NagibJohn T. Rettaliata Professor of Mechanical and Aerospace Engineering, ILLINOIS TECH (IIT)iit.edu üzerinde doğrulanmış e-posta adresine sahip
Marco AtzoriResearcher, DAER, Polimipolimi.it üzerinde doğrulanmış e-posta adresine sahip
Luca GuastoniTechnical University of Munichtum.de üzerinde doğrulanmış e-posta adresine sahip
Francesco Fuso NeriniKTH Royal Institute of Technologyenergy.kth.se üzerinde doğrulanmış e-posta adresine sahip
Sergio Hoyas (Orcid: 0000-0002-8458...Professor of Aerospace Engineering, Universitat Politècnica de Valènciamot.upv.es üzerinde doğrulanmış e-posta adresine sahip
Andrea IaniroProfessor of Aerospace Engineering, Universidad Carlos III de Madriding.uc3m.es üzerinde doğrulanmış e-posta adresine sahip
STEFANO DISCETTIUniversidad Carlos III de Madriding.uc3m.es üzerinde doğrulanmış e-posta adresine sahip
Dan HenningsonSwedish e-Science Research Centre (SeRC), FLOW Turbulence Laboratory, KTH Engineering Mechanciskth.se üzerinde doğrulanmış e-posta adresine sahip
Jean Rabaultmet.no üzerinde doğrulanmış e-posta adresine sahip
Soledad Le ClaincheETSIAE-UPMupm.es üzerinde doğrulanmış e-posta adresine sahip
Adrian Lozano-DuranCalifornia Institute of Technologycaltech.edu üzerinde doğrulanmış e-posta adresine sahip
Max TegmarkProfessor of Physics, MITmit.edu üzerinde doğrulanmış e-posta adresine sahip
Steven L. BruntonProfessor, University of Washingtonuw.edu üzerinde doğrulanmış e-posta adresine sahip
Francisco Alcántara ÁvilaResearcher, UPVLCalumni.upv.es üzerinde doğrulanmış e-posta adresine sahip
Oriol LehmkuhlLarge-scale Computational Fluid Dynamics Team Leader at Barcelona Supercomputer Center (BSC)bsc.es üzerinde doğrulanmış e-posta adresine sahip
Ivan MarusicUniversity of Melbourneunimelb.edu.au üzerinde doğrulanmış e-posta adresine sahip
Beverley McKeonStanford Universitystanford.edu üzerinde doğrulanmış e-posta adresine sahip
Paul FischerProfessor, Depts. of Computer Science and Mechanical Science & Engineering, University of Illinoismcs.anl.gov üzerinde doğrulanmış e-posta adresine sahip
Alexander J SmitsResearch Scholar, Professor Emeritus, MAE, Princeton Univ.princeton.edu üzerinde doğrulanmış e-posta adresine sahip

Takip et

Ricardo Vinuesa

Associate Professor, KTH Royal Institute of Technology

mech.kth.se üzerinde doğrulanmış e-posta adresine sahip - Ana Sayfa

Artificial Intelligence Simulation Turbulent boundary layers Flow control Sustainability


Başlık Alıntılara göre sırala Yıla göre sırala Başlığa göre sırala	Alıntı yapanlar Alıntı yapanlar	Yıl
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	2476	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	191	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	166	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	144	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	113	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	108	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	105	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

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