Hauptseite > Publikationsdatenbank > Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning > print

001     859970
005     20210511092514.0
020 _ _ |a 978-3-030-02464-2 (print)
020 _ _ |a 978-3-030-02465-9 (electronic)
024 7 _ |a 10.1007/978-3-030-02465-9_44
|2 doi
024 7 _ |a 0302-9743
|2 ISSN
024 7 _ |a 1611-3349
|2 ISSN
024 7 _ |a WOS:000612998200051
|2 WOS
037 _ _ |a FZJ-2019-00776
100 1 _ |a Bode, Mathis
|0 0000-0001-9922-9742
|b 0
111 2 _ |a International Conference on High Performance Computing
|c Frankfurt
|d 2018-06-24 - 2018-06-28
|w Germany
245 _ _ |a Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning
260 _ _ |a Cham
|c 2018
|b Springer International Publishing
295 1 0 |a High Performance Computing
300 _ _ |a 614 - 623
336 7 _ |a CONFERENCE_PAPER
|2 ORCID
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a Output Types/Conference Paper
|2 DataCite
336 7 _ |a Contribution to a conference proceedings
|b contrib
|m contrib
|0 PUB:(DE-HGF)8
|s 1620717880_29207
|2 PUB:(DE-HGF)
336 7 _ |a Contribution to a book
|0 PUB:(DE-HGF)7
|2 PUB:(DE-HGF)
|m contb
490 0 _ |a Lecture Notes in Computer Science
|v 11203
520 _ _ |a In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various generative adversarial networks (GANs) are discussed with respect to their suitability for understanding and modeling turbulence. Wasserstein GANs (WGANs) are then chosen to generate small-scale turbulence. Highly resolved direct numerical simulation (DNS) turbulent data is used for training the WGANs and the effect of network parameters, such as learning rate and loss function, is studied. Qualitatively good agreement between DNS input data and generated turbulent structures is shown. A quantitative statistical assessment of the predicted turbulent fields is performed.
536 _ _ |a 511 - Computational Science and Mathematical Methods (POF3-511)
|0 G:(DE-HGF)POF3-511
|c POF3-511
|f POF III
|x 0
536 _ _ |a Using deep learning to predict statistics of turbulent flows at high Reynolds numbers (jhpc55_20180501)
|0 G:(DE-Juel1)jhpc55_20180501
|c jhpc55_20180501
|f Using deep learning to predict statistics of turbulent flows at high Reynolds numbers
|x 1
588 _ _ |a Dataset connected to CrossRef Book Series
700 1 _ |a Gauding, Michael
|0 0000-0003-0038-5249
|b 1
|e Corresponding author
700 1 _ |a Göbbert, Jens Henrik
|0 P:(DE-Juel1)168541
|b 2
700 1 _ |a Liao, Baohao
|0 0000-0001-8335-4573
|b 3
700 1 _ |a Jitsev, Jenia
|0 P:(DE-Juel1)158080
|b 4
700 1 _ |a Pitsch, Heinz
|0 0000-0001-5656-0961
|b 5
770 _ _ |a ISC High Performance 2018 International Workshops
773 _ _ |a 10.1007/978-3-030-02465-9_44
909 C O |p VDB
|o oai:juser.fz-juelich.de:859970
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)168541
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)158080
913 1 _ |a DE-HGF
|b Key Technologies
|l Supercomputing & Big Data
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-511
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-500
|4 G:(DE-HGF)POF
|v Computational Science and Mathematical Methods
|x 0
913 2 _ |a DE-HGF
|b Programmungebundene Forschung
|l ohne Programm
|1 G:(DE-HGF)POF4-890
|0 G:(DE-HGF)POF4-899
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-800
|4 G:(DE-HGF)POF
|v ohne Topic
|x 0
914 1 _ |y 2018
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a contrib
980 _ _ |a VDB
980 _ _ |a contb
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21