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Machine-Learning Methods for Complex Flows
Vinuesa, Ricardo
KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics.
ORCID iD:
0000-0001-6570-5499
Le Clainche, Soledad
Univ Politecn Madrid, Sch Aerosp Engn, Madrid 28040, Spain..
2022 (English)
In:
Energies, E-ISSN 1996-1073, Vol. 15, no 4, article id 1513
Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
MDPI AG , 2022. Vol. 15, no 4, article id 1513
National Category
Fluid Mechanics Mathematical Analysis Algebra and Logic
Identifiers
URN:
urn:nbn:se:kth:diva-314834
DOI:
10.3390/en15041513
ISI:
000805156400046
Scopus ID:
2-s2.0-85125101841
OAI: oai:DiVA.org:kth-314834
DiVA, id:
diva2:1676839
Note
QC 20220627
Available from:
2022-06-27
Created:
2022-06-27
Last updated:
2025-02-09
Bibliographically approved
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Vinuesa, Ricardo
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Energies
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ieee
modern-language-association-8th-edition
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apa
ieee
modern-language-association-8th-edition
vancouver
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de-DE
en-GB
en-US
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nn-NB
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