Low-Dimensional Flow Models from High-Dimensional Flow Data with Machine Learning and First Principles

被引：0

作者：

Deng, Nan ^{[1
,2
]}

Pastur, Luc R. ^{[1
]}

Noack, Bernd R. ^{[3
]}

机构：

[1] ENSTA Paris, IP Paris, IMSIA, Paris, France

[2] UPSaclay, LIMSI, Gif Sur Yvette, France

[3] Harbin Inst Technol, Harbin, Heilongjiang, Peoples R China

来源：

ERCIM NEWS | 2020年 / 122期

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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 human-interpretable model from complex high-dimensional data using machine learning and first principles.

引用

页码：30 / 31

页数：2

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