⇦ Using deep learning to predict statistics of turbulent flows at high Reynolds numbers

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http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Journal Article Bode, M. (Corresponding author)FZJ*
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Large-Eddy Simulations of ECN Spray C
SAE international journal of advances and current practices in mobility 4(6), 2211-2219 (2022) [10.4271/2022-01-0503]2022   Files  Fulltext by OpenAccess repository BibTeX | EndNote: XML, Text | RIS

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Contribution to a conference proceedings/Contribution to a book Bode, M. (Corresponding author)FZJ*
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Large-Eddy Simulations of ECN Spray C
2022SAE WCX World Congress Experience, DetroitDetroit, USA, 5 Apr 2022 - 7 Apr 20222022-04-052022-04-07 SAE Technical Paper 1-9 (2022)2022   Files  Fulltext by OpenAccess repository BibTeX | EndNote: XML, Text | RIS

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Journal Article Bode, M. (Corresponding author)FZJ* ; Gauding, M. ; Lian, Z. ; Denker, D. ; Davidovic, M. ; Kleinheinz, K. ; Jitsev, J.FZJ* ; Pitsch, H.
Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows
Proceedings of the Combustion Institute 38(2), 2617 - 2625 (2021) [10.1016/j.proci.2020.06.022]2021   Files  Fulltext by OpenAccess repository BibTeX | EndNote: XML, Text | RIS

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