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Other Publications Year : 2020

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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Dates and versions

hal-03195632 , version 1 (12-04-2021)

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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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