12 citations · 16 across the 6 of their papers we have counts for
4 papers · 1 filter
SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms
Maochao Xiao, Yuning Wang, Felix Rodach +15
Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and disco…
Deep-reinforcement-learning-based separation control in a two-dimensional airfoil
Xavier Garcia, Arnau Miró, Pol Suárez +5
The aim of this study is to discover new active-flow-control (AFC) techniques for separation mitigation in a two-dimensional NACA 0012 airfoil at a Reynolds number of 3000. To find…
Active flow control for three-dimensional cylinders through deep reinforcement learning
Pol Suárez, Francisco Alcántara-Ávila, Arnau Miró +4
This paper presents for the first time successful results of active flow control with multiple independently controlled zero-net-mass-flux synthetic jets. The jets are placed on a…
Deep reinforcement learning for flow control exploits different physics for increasing Reynolds-number regimes
Pau Varela, Pol Suárez, Francisco Alcántara-Ávila +6
Deep artificial neural networks (ANNs) used together with deep reinforcement learning (DRL) are receiving growing attention due to their capabilities to control complex problems. T…