Deep Reinforcement Learning for Flow Control Exploits Different Physics for Increasing Reynolds Number Regimes
Pau Varela(Universitat Politècnica de València), Ricardo Vinuesa(Swedish e-Science Research Centre), O. Lehmkuhl(Barcelona Supercomputing Center), Arnau Miró(Barcelona Supercomputing Center), Jean Rabault(Norwegian Meteorological Institute), Pol Suárez(Barcelona Supercomputing Center), Francisco Alcántara-Ávila(KTH Royal Institute of Technology), Luis Miguel García-Cuevas(Universitat Politècnica de València), Bernat Font(Delft University of Technology)
Cited by 52
Related Papers
The role of artificial intelligence in achieving the Sustainable Development Goals
|Nature Communications|2020|2.9k
Predictive Factors for Invasive Disease Due to Penicillin-Resistant Streptococcus pneumoniae: A Population-Based Study
|Clinical Infectious Diseases|1994|219
Clinical and Laboratory Characteristics of 144 Patients with Mediterranean Spotted Fever
|European Journal of Clinical Microbiology & Infectious Diseases|2003|212
Direct numerical simulation of the flow over a sphere at <i>Re</i> = 3700
|Journal of Fluid Mechanics|2011|175
Low-frequency unsteadiness in the vortex formation region of a circular cylinder
|Physics of Fluids|2013|163