001     917410
005     20230123101928.0
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037 _ _ |a FZJ-2023-00624
041 _ _ |a English
100 1 _ |a Bode, Mathis
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111 2 _ |a SAE WCX World Congress Experience
|c Detroit
|d 2022-04-05 - 2022-04-07
|w USA
245 _ _ |a Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Large-Eddy Simulations of ECN Spray C
260 _ _ |c 2022
300 _ _ |a 1-9
336 7 _ |a CONFERENCE_PAPER
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336 7 _ |a Conference Paper
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336 7 _ |a INPROCEEDINGS
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336 7 _ |a conferenceObject
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336 7 _ |a Output Types/Conference Paper
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336 7 _ |a Contribution to a conference proceedings
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336 7 _ |a Contribution to a book
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490 0 _ |a SAE Technical Paper
536 _ _ |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5112
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536 _ _ |a Using deep learning to predict statistics of turbulent flows at high Reynolds numbers (jhpc55_20190501)
|0 G:(DE-Juel1)jhpc55_20190501
|c jhpc55_20190501
|f Using deep learning to predict statistics of turbulent flows at high Reynolds numbers
|x 1
770 _ _ |z 2022-01-0503
773 _ _ |y 2022
856 4 _ |u https://juser.fz-juelich.de/record/917410/files/dlc_06.pdf
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909 C O |o oai:juser.fz-juelich.de:917410
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910 1 _ |a Forschungszentrum Jülich
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913 1 _ |a DE-HGF
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|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
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|v Enabling Computational- & Data-Intensive Science and Engineering
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914 1 _ |y 2022
915 _ _ |a OpenAccess
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920 1 _ |0 I:(DE-Juel1)JSC-20090406
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980 _ _ |a I:(DE-Juel1)JSC-20090406
980 1 _ |a FullTexts


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