J Rabault
J Rabault
Unknown affiliation
Verified email at
Cited by
Cited by
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
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
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
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
Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach
J Rabault, A Kuhnle
Physics of Fluids 31 (9), 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
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
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
Observations of wave dispersion and attenuation in landfast ice
G Sutherland, J Rabault
Journal of Geophysical Research: Oceans, 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
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
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
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
A two layer model for wave dissipation in sea ice
G Sutherland, J Rabault, K Christensen, A Jensen
Applied Ocean Research, 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
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, Ř Breivik
Cold Regions Science and Technology 170, 102955, 2020
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
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
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
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
The system can't perform the operation now. Try again later.
Articles 1–20