| 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 | 390 | 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 | 197 | 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 | 168 | 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 | 161 | 2021 |
| Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach J Rabault, A Kuhnle Physics of Fluids 31 (9), 2019 | 150 | 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 | 128 | 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 | 109 | 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 | 106 | 2020 |
| Observations of wave dispersion and attenuation in landfast ice G Sutherland, J Rabault Journal of Geophysical Research: Oceans, 2016 | 79 | 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 | 63 | 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 | 62 | 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 | 61 | 2020 |
| A two layer model for wave dissipation in sea ice G Sutherland, J Rabault, K Christensen, A Jensen Applied Ocean Research, 2019 | 60 | 2019 |
| 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 | 55 | 2023 |
| 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 | 51 | 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 | 50* | 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 | 47 | 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 | 47 | 2016 |
| 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 | 44 | 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 | 43 | 2017 |
Atle JensenDepartment of Mathematics, University of Oslo在 math.uio.no 的电子邮件经过验证
