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Challenges of deep unsupervised optical flow estimation for particle-image velocimetry data

; ; ;

In
Experiments in fluids 65(3), Seiten/Artikel-Nr.:30

ImpressumBerlin ; Heidelberg [u.a.] : Springer

Umfang[1]-18

ISSN1432-1114

Online
DOI: 10.1007/s00348-024-03768-2


Einrichtungen

  1. Lehrstuhl für Strömungslehre und Aerodynamisches Institut (415110)
  2. JARA-CSD (Center for Simulation and Data Science) (080031)


Thematische Einordnung (Klassifikation)
DDC: 530


Dokumenttyp
Journal Article

Format
online, print

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85185507622
WOS Core Collection: WOS:001164543900001

Interne Identnummern
RWTH-2024-03011
Datensatz-ID: 981415

Beteiligte Länder
Germany, UK, USA

Lizenzstatus der Zeitschrift

 GO

Medline ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; DEAL Springer ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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The record appears in these collections:
Document types > Articles > Journal Articles
Faculty of Mechanical Engineering (Fac.4)
Central and Other Institutions
Public records
Publications database
080031
415110
 Record created 2024-03-11, last modified 2025-10-07
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