J Rabault

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Atle JensenDepartment of Mathematics, University of OsloVerified email at math.uio.no
Graig SutherlandEnvironment and Climate Change CanadaVerified email at canada.ca
Aleksey MarchenkoThe University Centre in SvalbardVerified email at unis.no
Kai Håkon ChristensenHead of Division for Ocean and Ice, Norwegian Meteorological InstituteVerified email at met.no
Ricardo VinuesaAssociate Professor, KTH Royal Institute of TechnologyVerified email at mech.kth.se
Alexander KuhnleBlue PrismVerified email at cantab.ac.uk
Ulysse REGLADECivil Engineer, Ecole des Mines de ParisVerified email at mines-paristech.fr
Jonathan ViqueratMines ParisTech - CEMEFVerified email at mines-paristech.fr
Joey VoermansUniversity of Melbourne, Ocean EngineeringVerified email at unimelb.edu.au
Elie HachemProfessor at MINES Paris PSL - ERC_Cog_2021Verified email at mines-paristech.fr
Miroslav KuchtaSimula Research LaboratoryVerified email at simula.no
takuji wasedaThe University of TokyoVerified email at k.u-tokyo.ac.jp
Alexander BabaninThe University of MelbourneVerified email at unimelb.edu.au
Nicolas CerardiPhD Student, CEA AIMVerified email at cea.fr
Oyvind BreivikHead of Divison for Oceanography and Marine Meteorology, Norwegian Meteorological InstituteVerified email at met.no
Tsubasa KodairaOcean Technology, Policy, and Environment, GSFS, The University of TokyoVerified email at edu.k.u-tokyo.ac.jp
Müller MalteSenior Scientist, Norwegian Meteorological Institute; Professor II, University of OsloVerified email at met.no
Hui TangThe Hong Kong Polytechnic UniversityVerified email at polyu.edu.hk
Oriol LehmkuhlLarge-scale Computational Fluid Dynamics Team Leader at Barcelona Supercomputer Center (BSC)Verified email at bsc.es
Trygve LøkenAssociate professor at Nord UniversityVerified email at nord.no

J Rabault

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Verified email at met.no


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	319	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	154	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	138	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	130	2021
Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach J Rabault, A Kuhnle Physics of Fluids 31 (9), 2019	121	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	98	2021
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	98	2017
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	83	2020
Observations of wave dispersion and attenuation in landfast ice G Sutherland, J Rabault Journal of Geophysical Research: Oceans, 2016	72	2016
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	59	2019
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	54	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	52	2020
A two layer model for wave dissipation in sea ice G Sutherland, J Rabault, K Christensen, A Jensen Applied Ocean Research, 2019	52	2019
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	45	2016
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	43*	2020
Curving to fly: Synthetic adaptation unveils optimal flight performance of whirling fruits J Rabault, RA Fauli, A Carlson Physical Review Letters 122 (2), 024501, 2019	41	2019
Measurements of wave damping by a grease ice slick in Svalbard using off-the-shelf sensors and open source electronics J Rabault, G Sutherland, O Gundersen, A Jensen Journal of Glaciology, 2017	41	2017
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	39	2022
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	30	2023
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	27	2023

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