572 citations · 2.1k across the 54 of their papers we have counts for
9 papers · 1 filter
Wave measurements from ship mounted sensors in the Arctic marginal ice zone
Trygve K. Løken, Jean Rabault, Atle Jensen +3
Increased research interest and economic activity in the Arctic raise the need for new observations of sea ice dynamics. Remote sensing as well as mathematical and numerical models…
Exploiting locality and physical invariants to design effective Deep Reinforcement Learning control of the unstable falling liquid film
Vincent Belus, Jean Rabault, Jonathan Viquerat +3
Instabilities arise in a number of flow configurations. One such manifestation is the development of interfacial waves in multiphase flows, such as those observed in the falling li…
Direct shape optimization through deep reinforcement learning
Jonathan Viquerat, Jean Rabault, Alexander Kuhnle +3
Deep Reinforcement Learning (DRL) has recently spread into a range of domains within physics and engineering, with multiple remarkable achievements. Still, much remains to be explo…
A review on Deep Reinforcement Learning for Fluid Mechanics
Paul Garnier, Jonathan Viquerat, Jean Rabault +3
Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics and engineering domains for its ability to solve decision-making problems that were previousl…
Wave-ice interaction in the North-West Barents Sea
Aleksey Marchenko, Peter Wadhams, Clarence O Collins +2
The results of field work on drift ice during wave propagation are analyzed and presented. The field work was performed in the Barents Sea, and the main focus of the paper is on wa…
Accelerating Deep Reinforcement Learning strategies of Flow Control through a multi-environment approach
Jean Rabault, Alexander Kuhnle
Deep Reinforcement Learning (DRL) has recently been proposed as a methodology to discover complex Active Flow Control (AFC) strategies [Rabault, J., Kuchta, M., Jensen, A., Reglade…