Jean Rabault
Jean Rabault
Senior Engineer, Norwegian Meteorological Institute
Verified email at - Homepage
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
Observations of wave dispersion and attenuation in landfast ice
G Sutherland, J Rabault
Journal of Geophysical Research: Oceans, 2016
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
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
Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach
J Rabault, A Kuhnle
Physics of Fluids 31 (9), 094105, 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
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
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
A two layer model for wave dissipation in sea ice
G Sutherland, J Rabault, K Christensen, A Jensen
Applied Ocean Research, 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
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
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
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), 125014, 2019
The dynamics of a capsule in a wall-bounded oscillating shear flow
LL Zhu, J Rabault, L Brandt
Physics of Fluids 27 (7), 071902, 2015
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
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
Field observations and preliminary investigations of a wave event in solid drift ice in the Barents Sea
A Marchenko, J Rabault, G Sutherland, COIII Collins, P Wadhams, ...
24th International Conference on Port and Ocean Engineering under Arctic …, 2017
Wave-ice interaction in the North-West Barents Sea
A Marchenko, P Wadhams, C Collins, J Rabault, M Chumakov
Applied Ocean Research 90, 101861, 2019
The attenuation of monochromatic surface waves due to the presence of an inextensible cover
G Sutherland, T Halsne, J Rabault, A Jensen
Wave motion, 2017
Measurements of Waves in Landfast Ice Using Inertial Motion Units
J Rabault, G Sutherland, B Ward, KH Christensen, T Halsne, A Jensen
IEEE Transactions on Geoscience and Remote Sensing, 2016
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