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@INPROCEEDINGS{Bode:874553,
author = {Bode, Mathis and Denker, Dominik and Jitsev, Jenia and
Pitsch, Heinz},
title = {{S}ub-{G}rid {S}cale {M}odelling at {S}cale with {D}eep
{L}earning and up to 60 {B}illion {D}egrees of {F}reedom},
volume = {50},
address = {Jülich},
publisher = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
reportid = {FZJ-2020-01507},
series = {Publication Series of the John von Neumann Institute for
Computing (NIC) NIC Series},
pages = {379 - 388},
year = {2020},
comment = {NIC Symposium 2020},
booktitle = {NIC Symposium 2020},
abstract = {This work presents fully resolved direct numerical
simulations (DNSs) of a turbulent reactive planar temporally
non-premixed jet configuration with up to 60 billion degrees
of freedom. As scalar mixing is of utmost importance for
this kind of configuration, a novel deep learning (DL)
approach in the context of large-eddy simulation is
presented which results in predictive mixing statistics on
underresolved grids. The usability of the mixing model is
approved by applying it to the DNS data. Furthermore, node
performance measurements for the training of the DL networks
are shown for different computing clusters.},
month = {Feb},
date = {2020-02-27},
organization = {NIC Symposium 2020, Jülich (Germany),
27 Feb 2020 - 28 Feb 2020},
cin = {NIC / JSC},
cid = {I:(DE-Juel1)NIC-20090406 / I:(DE-Juel1)JSC-20090406},
pnm = {512 - Data-Intensive Science and Federated Computing
(POF3-512)},
pid = {G:(DE-HGF)POF3-512},
typ = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
url = {https://juser.fz-juelich.de/record/874553},
}