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Low-dimensional Flow Models from high-dimensional Flow data with Machine Learning and First Principles

Abstract : Reduced-order modelling and system identification can help us figure out the elementary degrees of freedom and the underlying mechanisms from the high-dimensional and nonlinear dynamics of fluid flow. Machine learning has brought new opportunities to these two processes and is revolutionising traditional methods. We show a framework to obtain a sparse humaninterpretable model from complex high-dimensional data using machine learning and first principles.
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https://hal-ensta-paris.archives-ouvertes.fr/hal-03195632
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Submitted on : Monday, April 12, 2021 - 12:15:09 AM
Last modification on : Thursday, October 27, 2022 - 1:45:02 PM
Long-term archiving on: : Tuesday, July 13, 2021 - 6:09:38 PM

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  • HAL Id : hal-03195632, version 1

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Deng Nan, Luc R. Pastur, Bernd R. Noack. Low-dimensional Flow Models from high-dimensional Flow data with Machine Learning and First Principles. ERCIM News 122: Solving Engineering Problems with Machine Learning., 2020. ⟨hal-03195632⟩

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