J Rabault

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Atle JensenDepartment of Mathematics, University of OsloAdresse e-mail validée de math.uio.no
Graig SutherlandEnvironment and Climate Change CanadaAdresse e-mail validée de canada.ca
Ricardo VinuesaAssociate Professor, KTH Royal Institute of TechnologyAdresse e-mail validée de mech.kth.se
Aleksey MarchenkoThe University Centre in SvalbardAdresse e-mail validée de unis.no
Alexander KuhnleZebra TechnologiesAdresse e-mail validée de cantab.ac.uk
Kai H. ChristensenSenior Scientist, Norwegian Meteorological InstituteAdresse e-mail validée de met.no
Oyvind BreivikHead of Divison for Oceanography and Marine Meteorology, Norwegian Meteorological InstituteAdresse e-mail validée de met.no
Joey VoermansUniversity of Melbourne, Ocean EngineeringAdresse e-mail validée de unimelb.edu.au
Ulysse REGLADECivil Engineer, Ecole des Mines de ParisAdresse e-mail validée de mines-paristech.fr
Jonathan ViqueratMines ParisTech - CEMEFAdresse e-mail validée de mines-paristech.fr
Miroslav KuchtaSimula Research LaboratoryAdresse e-mail validée de simula.no
Oriol LehmkuhlLarge-scale Computational Fluid Dynamics Team Leader at Barcelona Supercomputer Center (BSC)Adresse e-mail validée de bsc.es
Francisco Alcántara ÁvilaResearcher, UPVLCAdresse e-mail validée de alumni.upv.es
Elie HachemProfessor at MINES Paris PSL - ERC_Cog_2021Adresse e-mail validée de mines-paristech.fr
takuji wasedaThe University of TokyoAdresse e-mail validée de k.u-tokyo.ac.jp
Alexander BabaninThe University of MelbourneAdresse e-mail validée de unimelb.edu.au
Tsubasa KodairaOcean Technology, Policy, and Environment, GSFS, The University of TokyoAdresse e-mail validée de edu.k.u-tokyo.ac.jp
Nicolas CerardiPhD Student, CEA AIMAdresse e-mail validée de cea.fr
Müller MalteSenior Scientist, Norwegian Meteorological Institute; Professor II, University of OsloAdresse e-mail validée de met.no
Bernat FontTU DelftAdresse e-mail validée de tudelft.nl

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J Rabault

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Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Artificial Neural Networks trained through Deep Reinforcement Learning discover control strategies for active flow control J Rabault, M Kuchta, A Jensen, U Reglade, N Cerardi Journal of Fluid Mechanics, 2019	434	2019
A review on Deep Reinforcement Learning for Fluid Mechanics P Garnier, J Viquerat, J Rabault, A Larcher, A Kuhnle, E Hachem Computers and Fluids, 2021	235	2021
Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning H Tang, J Rabault, A Kuhnle, Y Wang, T Wang Physics of Fluids, 2020	186	2020
Direct shape optimization through deep reinforcement learning J Viquerat, J Rabault, A Kuhnle, H Ghraieb, A Larcher, E Hachem Journal of Computational Physics 428, 110080, 2021	177	2021
Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach J Rabault, A Kuhnle Physics of Fluids 31 (9), 2019	164	2019
Applying deep reinforcement learning to active flow control in weakly turbulent conditions F Ren, J Rabault, H Tang Physics of Fluids 33 (3), 2021	140	2021
Deep reinforcement learning in fluid mechanics: A promising method for both active flow control and shape optimization J Rabault, F Ren, W Zhang, H Tang, H Xu Journal of Hydrodynamics, 2020	130	2020
Performing particle image velocimetry using artificial neural networks: a proof-of-concept J Rabault, J Kolaas, A Jensen Measurement Science and Technology 28 (12), 125301, 2017	114	2017
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	82	2023
Observations of wave dispersion and attenuation in landfast ice G Sutherland, J Rabault Journal of Geophysical Research: Oceans, 2016	81	2016
Exploiting locality and translational invariance to design effective deep reinforcement learning control of the 1-dimensional unstable falling liquid film V Belus, J Rabault, J Viquerat, Z Che, E Hachem, U Reglade AIP Advances 9 (12), 2019	68	2019
Active flow control with rotating cylinders by an artificial neural network trained by deep reinforcement learning H Xu, W Zhang, J Deng, J Rabault Journal of Hydrodynamics, 2020	67	2020
Experiments on wave propagation in grease ice: combined wave gauges and particle image velocimetry measurements J Rabault, G Sutherland, A Jensen, KH Christensen, A Marchenko Journal of Fluid Mechanics 864, 876-898, 2019	66	2019
Deep reinforcement learning for turbulent drag reduction in channel flows L Guastoni, J Rabault, P Schlatter, H Azizpour, R Vinuesa The European Physical Journal E 46 (4), 27, 2023	63	2023
A two layer model for wave dissipation in sea ice G Sutherland, J Rabault, K Christensen, A Jensen Applied Ocean Research, 2019	61	2019
Flow control in wings and discovery of novel approaches via deep reinforcement learning R Vinuesa, O Lehmkuhl, A Lozano-Durán, J Rabault Fluids 7 (2), 62, 2022	57	2022
An open source, versatile, affordable waves in ice instrument for scientific measurements in the Polar Regions J Rabault, G Sutherland, O Gundersen, A Jensen, A Marchenko, Ø Breivik Cold Regions Science and Technology 170, 102955, 2020	53*	2020
DRLinFluids: An open-source Python platform of coupling deep reinforcement learning and OpenFOAM Q Wang, L Yan, G Hu, C Li, Y Xiao, H Xiong, J Rabault, BR Noack Physics of Fluids 34 (8), 2022	52	2022
A study using PIV of the intake flow in a diesel engine cylinder J Rabault, JA Vernet, B Lindgren, PH Alfredsson International Journal of Heat and Fluid Flow, 2016	49	2016
Comparative analysis of machine learning methods for active flow control F Pino, L Schena, J Rabault, MA Mendez Journal of Fluid Mechanics 958, A39, 2023	48	2023

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