Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

We present a new nonlinear mode decomposition method to visualize decomposed flow fields, named the mode decomposing convolutional neural network autoencoder (MD-CNN-AE). The proposed method is applied to a flow around a circular cylinder at the Reynolds number $Re_{D}=100$ as a test case. The flow...

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Vydané v:	Journal of fluid mechanics Ročník 882
Hlavní autori:	Murata, Takaaki, Fukami, Kai, Fukagata, Koji
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cambridge Cambridge University Press 10.01.2020
Predmet:	Activation Artificial neural networks Circular cylinders Computational fluid dynamics Cylinders Decomposition Dimensions Feature extraction Fields Flow mapping Fluid dynamics Fluid flow Fluid mechanics Hydrodynamics Machine learning Methods Modes Neural networks Nonlinear systems Nonlinearity Proper Orthogonal Decomposition Reynolds number Velocity
ISSN:	0022-1120, 1469-7645
On-line prístup:	Získať plný text
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