Hauptseite > Publikationsdatenbank > Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows > print

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005     20230215201830.0
024 7 _ |a 10.1016/j.proci.2020.06.022
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024 7 _ |a 1540-7489
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024 7 _ |a 1873-2704
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037 _ _ |a FZJ-2021-02100
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100 1 _ |a Bode, Mathis
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245 _ _ |a Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows
260 _ _ |a New York, NY [u.a.]
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536 _ _ |a Using deep learning to predict statistics of turbulent flows at high Reynolds numbers (jhpc55_20190501)
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|f Using deep learning to predict statistics of turbulent flows at high Reynolds numbers
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700 1 _ |a Gauding, Michael
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700 1 _ |a Lian, Zeyu
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700 1 _ |a Denker, Dominik
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700 1 _ |a Davidovic, Marco
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700 1 _ |a Kleinheinz, Konstantin
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700 1 _ |a Jitsev, Jenia
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700 1 _ |a Pitsch, Heinz
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773 _ _ |a 10.1016/j.proci.2020.06.022
|g Vol. 38, no. 2, p. 2617 - 2625
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|t Proceedings of the Combustion Institute
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856 4 _ |u https://juser.fz-juelich.de/record/892464/files/1-s2.0-S1540748920300481-main.pdf
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