2 citations · 4 across the 6 of their papers we have counts for
3 papers · 1 filter
Towards Active Flow Control Strategies Through Deep Reinforcement Learning
Ricard Montalà, Bernat Font, Pol Suárez +3
This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL a…
Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality
Joongoo Jeon, Jean Rabault, Joel Vasanth +3
Flow control is key to maximize energy efficiency in a wide range of applications. However, traditional flow-control methods face significant challenges in addressing non-linear sy…
Decoding complexity: how machine learning is redefining scientific discovery
Ricardo Vinuesa, Paola Cinnella, Jean Rabault +10
As modern scientific instruments generate vast amounts of data and the volume of information in the scientific literature continues to grow, machine learning (ML) has become an ess…