Skip to Main content Skip to Navigation
Other publications

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.
Complete list of metadata

https://hal-ensta-paris.archives-ouvertes.fr//hal-03195632
Contributor : Nan Deng Connect in order to contact the contributor
Submitted on : Monday, April 12, 2021 - 12:15:09 AM
Last modification on : Friday, June 25, 2021 - 3:37:36 AM
Long-term archiving on: : Tuesday, July 13, 2021 - 6:09:38 PM

File

ERCIM_DENG.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03195632, version 1

Citation

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⟩

Share

Metrics

Record views

16

Files downloads

9