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Journal articles
Machine learning strategies applied to the control of a fluidic pinball
C. Raibaudo 1
Author ORCID : https://orcid.org/0000-0001-7391-8312
P. Zhong 1
Author
B. Noack 2
Author
R. Martinuzzi 1
Author ORCID : https://orcid.org/0000-0003-4349-2896
2
LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (Bâtiment 507, rue du Belvédère,
91405, Orsay cedex - France)
- Université Paris-Saclay (Espace Technologique, Bat. Discovery - RD 128 - 2e ét., 91190 Saint-Aubin - France)
- CNRS - Centre National de la Recherche Scientifique : UPR 3251 (France)
Document type :
Journal articles
Complete list of metadata
https://hal-univ-orleans.archives-ouvertes.fr/hal-03371787
Contributor : Cédric Raibaudo Connect in order to contact the contributor
Submitted on : Friday, October 8, 2021 - 10:29:50 PM
Last modification on : Sunday, October 10, 2021 - 3:32:18 AM
Contributor : Cédric Raibaudo Connect in order to contact the contributor
Submitted on : Friday, October 8, 2021 - 10:29:50 PM
Last modification on : Sunday, October 10, 2021 - 3:32:18 AM
Identifiers
- HAL Id : hal-03371787, version 1
- DOI : 10.1063/1.5127202
Collections
UNIV-ORLEANS | INSMI | TDS-MACS | LIMSI | CNRS | LISN | GS-COMPUTER-SCIENCE | GS-ENGINEERING
Citation
C. Raibaudo, P. Zhong, B. Noack, R. Martinuzzi. Machine learning strategies applied to the control of a fluidic pinball. Physics of Fluids, American Institute of Physics, 2020, 32 (1), pp.015108. ⟨10.1063/1.5127202⟩. ⟨hal-03371787⟩
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