Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data-Reference-Cited by-同舟云学术

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Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data

Published:2020-09-01 Issue:9 Volume:32 Page:095110
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Fukami Kai¹^ORCID,Nakamura Taichi¹,Fukagata Koji¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, Keio University, Yokohama 223-8522, Japan

Funder

Japan Society for the Promotion of Science

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0020721

Reference62 articles.

1. Dynamic mode decomposition of numerical and experimental data

2. Modal Analysis of Fluid Flows: An Overview

3. Modal Analysis of Fluid Flows: Applications and Outlook

4. A hierarchy of low-dimensional models for the transient and post-transient cylinder wake

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