Taming the fluidic pinball with artificial intelligence control

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Maceda:2018:EFMC,

• author = "Guy Yoslan {Cornejo Maceda} and Bernd R. Noack and Francois Lusseyran and Marek Morzynski and Luc Pastur and Nan Deng",
• title = "Taming the fluidic pinball with artificial intelligence control",
• booktitle = "European Fluid Mechanics Conference",
• year = "2018",
• address = "Vienne, Austria",
• publisher = "HAL CCSD",
• month = sep # "~01",
• keywords = "genetic algorithms, genetic programming, AI, artificial intelligence control, fluidic pinball, control, machine learning, drag reduction, physics, mechanics, mechanics of the fluids",
• type = "info:eu-repo/semantics/conferenceObject",
• URL = "https://hal.archives-ouvertes.fr/hal-02387544",
• abstract = "The aim of this work is to develop a generic control strategy for nonlinear dynamics. This strategy is based on genetic programming, a machine learning technique for regression problems, that maps the sensor signals to the actuators in a unsupervised manner.It's a biological inspired method mimicking Darwin's natural selection: through and evolution process it derives a control law minimising a given objective.Genetic programming has been applied to a DNS of a 2D fluidic mechanic system, the fluidic pinball.Several search spaces including control laws built from periodic functions, sensor signals and time-delay sensor signals have been explored.For the fluidic pinball genetic programming managed a 46percent net drag saving, outperforming by 3.3percent the best open-loop control law found with a parametric study.Our contribution has been the acceleration of the learning process by avoiding the evaluations of redundant control laws, thus improving the learning rate by a factor 3.",
• annote = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur (LIMSI) ; Universite Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Universite - UFR d'Ingenierie (UFR 919) ; Sorbonne Universite (SU)-Sorbonne Universite (SU)-Universite Paris-Saclay-Universite Paris-Sud - Paris 11 (UP11)",
• bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
• contributor = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur",
• coverage = "Vienne, Austria",
• description = "International audience",
• identifier = "hal-02387544",
• language = "en",
• oai = "oai:HAL:hal-02387544v1",

}

Genetic Programming entries for Guy Yoslan Cornejo Maceda Bernd R Noack Francois Lusseyran Marek Morzynski Luc Pastur Nan Deng

Citations