4 papers
Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction
Federica Tonti, Ricardo Vinuesa
We propose a method combining Multi-Agent Deep Reinforcement Learning (MARL) and eXplainable Deep Learning (XDL) to reduce drag in wall-bounded turbulent flows. Taking as a baselin…
Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI
Oliver Welin Odeback, Arivazhagan Geetha Balasubramanian, Jonas Schollenberger +9
4D Flow Magnetic Resonance Imaging (4D Flow MRI) enables non-invasive quantification of blood flow and hemodynamic parameters. However, its clinical application is limited by low s…
Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learning
Trishit Mondal, Ricardo Vinuesa, Ameya D. Jagtap
Active flow control of compressible transonic shock-boundary layer interactions over a two-dimensional RAE2822 airfoil at Re = 50,000 is investigated using deep reinforcement learn…
Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning
Federica Tonti, Ricardo Vinuesa
Unmanned Aerial Vehicles (UAVs) are increasingly populating urban areas for delivery and surveillance purposes. In this work, we develop an optimal navigation strategy based on Dee…