Assessment of supervised machine learning methods for fluid flows
Assessment of supervised machine learning methods for fluid flows
About this item
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Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
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Scope and Contents
Contents
We apply supervised machine learning techniques to a number of regression problems in fluid dynamics. Four machine learning architectures are examined in terms of their characteristics, accuracy, computational cost, and robustness for canonical flow problems. We consider the estimation of force coefficients and wakes from a limited number of sensor...
Alternative Titles
Full title
Assessment of supervised machine learning methods for fluid flows
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_proquest_journals_2434384385
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2434384385
Other Identifiers
ISSN
0935-4964
E-ISSN
1432-2250
DOI
10.1007/s00162-020-00518-y
