Super-resolution reconstruction of turbulent flows with machine learning
Super-resolution reconstruction of turbulent flows with machine learning
About this item
Full title
Author / Creator
Publisher
Cambridge, UK: Cambridge University Press
Journal title
Language
English
Formats
Publication information
Publisher
Cambridge, UK: Cambridge University Press
Subjects
More information
Scope and Contents
Contents
We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field. Two machine learning models are developed, namely, the convolutional neural network (CNN) and the hybrid downsampled skip-connection/multi-scale (DSC/MS) models. These machine learning model...
Alternative Titles
Full title
Super-resolution reconstruction of turbulent flows with machine learning
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2221606648
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2221606648
Other Identifiers
ISSN
0022-1120
E-ISSN
1469-7645
DOI
10.1017/jfm.2019.238
