Jean Rabault

2.7k total citations · 3 hit papers
66 papers, 1.7k citations indexed

About

Jean Rabault is a scholar working on Atmospheric Science, Computational Mechanics and Oceanography. According to data from OpenAlex, Jean Rabault has authored 66 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Atmospheric Science, 27 papers in Computational Mechanics and 22 papers in Oceanography. Recurrent topics in Jean Rabault's work include Arctic and Antarctic ice dynamics (27 papers), Fluid Dynamics and Turbulent Flows (22 papers) and Model Reduction and Neural Networks (19 papers). Jean Rabault is often cited by papers focused on Arctic and Antarctic ice dynamics (27 papers), Fluid Dynamics and Turbulent Flows (22 papers) and Model Reduction and Neural Networks (19 papers). Jean Rabault collaborates with scholars based in Norway, Sweden and Japan. Jean Rabault's co-authors include Atle Jensen, Ricardo Vinuesa, Miroslav Kuchta, Nicolas Cerardi, Graig Sutherland, Jonathan Viquerat, Elie Hachem, Hui Xu, Alexander Kuhnle and Aurélien Larcher and has published in prestigious journals such as Physical Review Letters, Nature Communications and Journal of Fluid Mechanics.

In The Last Decade

Jean Rabault

57 papers receiving 1.7k citations

Hit Papers

Artificial neural networks trained through deep reinforce... 2019 2026 2021 2023 2019 2021 2023 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control
    2019 347 citations Jean Rabault, Miroslav Kuchta et al. Journal of Fluid Mechanics profile →
  2. A review on deep reinforcement learning for fluid mechanics
    2021 186 citations Jonathan Viquerat, Jean Rabault et al. profile →
  3. Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions
    2023 97 citations Jean Rabault, Ricardo Vinuesa et al. Physics of Fluids profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jean Rabault Norway 20 1.0k 704 473 272 194 66 1.7k
Chryssostomos Chryssostomidis United States 20 504 0.5× 133 0.2× 323 0.7× 92 0.3× 64 0.3× 95 1.3k
Nils Thuerey Germany 23 1.1k 1.0× 441 0.6× 243 0.5× 70 0.3× 17 0.1× 67 1.8k
Andrew Kurzawski United States 10 768 0.8× 660 0.9× 328 0.7× 110 0.4× 12 0.1× 20 1.3k
Kevin J. Maki United States 19 881 0.9× 89 0.1× 341 0.7× 56 0.2× 69 0.4× 92 1.3k
Roel Verstappen Netherlands 17 1.3k 1.2× 246 0.3× 293 0.6× 171 0.6× 31 0.2× 56 1.5k
Allan Peter Engsig‐Karup Denmark 18 423 0.4× 103 0.1× 98 0.2× 174 0.6× 333 1.7× 81 1.1k
Donghyun You South Korea 27 1.6k 1.6× 86 0.1× 1.0k 2.2× 154 0.6× 35 0.2× 102 2.3k
Željko Tuković Croatia 21 1.1k 1.1× 76 0.1× 328 0.7× 131 0.5× 36 0.2× 49 1.9k
Wenyang Duan China 25 1.2k 1.2× 52 0.1× 275 0.6× 130 0.5× 344 1.8× 170 2.1k
Jiaqing Kou China 24 1.4k 1.4× 1.1k 1.5× 781 1.7× 44 0.2× 10 0.1× 70 2.0k

Countries citing papers authored by Jean Rabault

Since Specialization
Citations

This map shows the geographic impact of Jean Rabault's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jean Rabault with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jean Rabault more than expected).

Fields of papers citing papers by Jean Rabault

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jean Rabault. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jean Rabault. The network helps show where Jean Rabault may publish in the future.

Co-authorship network of co-authors of Jean Rabault

This figure shows the co-authorship network connecting the top 25 collaborators of Jean Rabault. A scholar is included among the top collaborators of Jean Rabault based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jean Rabault. Jean Rabault is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Breivik, Øyvind, et al.. (2025). An analysis on OpenMetBuoy-v2021 drifter in-situ data and Lagrangian trajectory simulations in the Agulhas Current System. Applied Ocean Research. 161. 104649–104649.
2.
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Rabault, Jean, et al.. (2025). Navigation in a simplified urban flow through deep reinforcement learning. Journal of Computational Physics. 538. 114194–114194. 1 indexed citations
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Rabault, Jean, et al.. (2025). Direct in-situ observations of wave-induced floe collisions in the deeper Marginal Ice Zone. Cold Regions Science and Technology. 240. 104642–104642.
6.
Font, Bernat, Francisco Alcántara-Ávila, Jean Rabault, Ricardo Vinuesa, & O. Lehmkuhl. (2025). Deep reinforcement learning for active flow control in a turbulent separation bubble. Nature Communications. 16(1). 1422–1422. 15 indexed citations
7.
Cavaleri, Luigi, Victor Alari, Alvise Benetazzo, et al.. (2025). More Room at the Top: How Small Buoys Help Reveal the Detailed Dynamics of the Air–Sea Interface. Bulletin of the American Meteorological Society. 106(6). E1063–E1076. 1 indexed citations
8.
Jensen, Atle, et al.. (2024). Grid Turbulence Measurements with an Acoustic Doppler Current Profiler. Fluids. 9(3). 60–60.
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Bohlinger, Patrik, Øyvind Breivik, Kai H. Christensen, et al.. (2023). Wave measurements using open source ship mounted ultrasonic altimeter and motion correction system during the one ocean circumnavigation. Ocean Engineering. 292. 116586–116586. 7 indexed citations
11.
12.
Rabault, Jean, et al.. (2023). Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions. Physics of Fluids. 35(3). 97 indexed citations breakdown →
13.
Sutherland, Graig, et al.. (2022). Estimating a mean transport velocity in the marginal ice zone using ice–ocean prediction systems. ˜The œcryosphere. 16(5). 2103–2114. 3 indexed citations
14.
Guastoni, Luca, et al.. (2021). Deep Reinforcement Learning for Active Drag Reduction in Wall Turbulence. Bulletin of the American Physical Society. 1 indexed citations
15.
Rabault, Jean, et al.. (2021). Bringing optical fluid motion analysis to the field: a methodology using an open source ROV as a camera system and rising bubbles as tracers. Measurement Science and Technology. 32(9). 95302–95302. 6 indexed citations
16.
Rabault, Jean, Graig Sutherland, Atle Jensen, Kai H. Christensen, & Aleksey Marchenko. (2019). Experiments on wave propagation in grease ice: combined wave gauges and particle image velocimetry measurements. Duo Research Archive (University of Oslo). 41 indexed citations
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Rabault, Jean & Alexander Kuhnle. (2019). Accelerating Deep Reinforcement Learning of Active Flow Control strategies through a multi-environment approach. arXiv (Cornell University). 3 indexed citations
19.
Rabault, Jean, et al.. (2018). An Artificial Neural Network trained through Deep Reinforcement Learning achieves Active Flow Control of the 2D Karman Vortex Street behind a Cylinder at moderate Reynolds number. Bulletin of the American Physical Society. 1 indexed citations
20.
Alfredsson, P. Henrik, et al.. (2015). PIV investigation of the intake flow in a parallel valves diesel engine cylinder. Bulletin of the American Physical Society. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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