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
收藏
页码：30 / 31
页数：2
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