Machine learning strategies applied to the control of a fluidic pinball

C. Raibaudo; P. Zhong; B. Noack; R. Martinuzzi

doi:10.1063/1.5127202

Journal Articles Physics of Fluids Year : 2020

Machine learning strategies applied to the control of a fluidic pinball

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C. Raibaudo

Function : Author
PersonId : 792377
ORCID : 0000-0001-7391-8312

Schulich School of Engineering, The University of Calgary, Calgary, AB T2N 1N4, Canada

P. Zhong

Function : Author

Schulich School of Engineering, The University of Calgary, Calgary, AB T2N 1N4, Canada

B. Noack

Function : Author

Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur

R. Martinuzzi

Function : Author
PersonId : 797403
ORCID : 0000-0003-4349-2896

Schulich School of Engineering, The University of Calgary, Calgary, AB T2N 1N4, Canada

Domains

Engineering Sciences [physics] Mechanics [physics.med-ph] Fluids mechanics [physics.class-ph] Statistics [stat] Machine Learning [stat.ML] Mathematics [math] Optimization and Control [math.OC]

Cédric Raibaudo : Connect in order to contact the contributor

https://hal-univ-orleans.archives-ouvertes.fr/hal-03371787

Submitted on : Friday, October 8, 2021-10:29:50 PM

Last modification on : Wednesday, March 16, 2022-3:54:02 AM

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hal-03371787 , version 1 (08-10-2021)

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HAL Id : hal-03371787 , version 1
DOI : 10.1063/1.5127202

Cite

C. Raibaudo, P. Zhong, B. Noack, R. Martinuzzi. Machine learning strategies applied to the control of a fluidic pinball. Physics of Fluids, 2020, 32 (1), pp.015108. ⟨10.1063/1.5127202⟩. ⟨hal-03371787⟩

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Machine learning strategies applied to the control of a fluidic pinball

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