572 citations · 2.1k across the 54 of their papers we have counts for
4 papers · 2 filters
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…
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…
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,…
Deep Reinforcement Learning in Fluid Mechanics: a promising method for both Active Flow Control and Shape Optimization
Jean Rabault, Feng Ren, Wei Zhang +2
In recent years, Artificial Neural Networks (ANNs) and Deep Learning have become increasingly popular across a wide range of scientific and technical fields, including Fluid Mechan…