activity
20152024
most citedArtificial Neural Networks trained through Deep Reinforcement Learning discover control strategies for active flow control

572 citations · 2.1k across the 54 of their papers we have counts for

collaborators
Showing 2020Show all

7 papers · 1 filter

physics.flu-dyn2020★ 11 cited

Bringing optical fluid motion analysis to the field: a methodology using an open source ROV as camera system and rising bubbles as tracers

Trygve K. Løken, Thea J. Ellevold, Reyna G. Ramirez de la Torre +2

Detailed water kinematics are important for understanding atmosphere-ice-ocean energy transfer processes in the Arctic. There are few in situ observations of 2D velocity fields in…

physics.flu-dyn2020★ 166 cited

Applying deep reinforcement learning to active flow control in turbulent conditions

Feng Ren, Jean Rabault, Hui Tang

Machine learning has recently become a promising technique in fluid mechanics, especially for active flow control (AFC) applications. A recent work [J. Fluid Mech. (2019), vol. 865…

physics.ao-ph2020★ 33 cited

Experimental evidence for a universal threshold characterizing wave-induced sea ice break-up

J. J. Voermans, J. Rabault, K. Filchuk +7

Waves can drastically transform a sea ice cover by inducing break-up over vast distances in the course of a few hours. However, relatively few detailed studies have described this…

physics.flu-dyn2020★ 216 cited

Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning

Hongwei Tang, Jean Rabault, Alexander Kuhnle +2

This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL). More precisely,…

physics.ao-ph2020

Development of open source instruments for in-situ measurements of waves in ice

Jean Rabault, Joey Voermans, Graig Sutherland +3

The interaction between surface waves and sea ice involves many complex physical phenomena such as viscous damping, wave diffraction, and nonlinear effects. The combination of thes…

physics.ao-ph2020★ 3 cited

A comparison of wave observations in the Arctic marginal ice zone with spectral models

Trygve K. Løken, Jean Rabault, Erin E. Thomas +4

Increased economic activity and research interest in the Arctic raise the need for better wave forecasts in the marginal ice zone (MIZ). Mathematical and numerical models of wave p…