Journal Article

FZJ-2021-02100

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows

Bode, M. (Corresponding author)FZJ* ; Gauding, M. ; Lian, Z. ; Denker, D. ; Davidovic, M. ; Kleinheinz, K. ; Jitsev, J.FZJ* ; Pitsch, H.

2021
Elsevier ScienceDirect New York, NY [u.a.]

This record in other databases:  

Please use a persistent id in citations: http://hdl.handle.net/2128/30393  doi:10.1016/j.proci.2020.06.022

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Contributing Institute(s):

1. JARA - HPC (JARA-HPC)
2. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. Using deep learning to predict statistics of turbulent flows at high Reynolds numbers (jhpc55_20190501) (jhpc55_20190501)

Appears in the scientific report 2021

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Essential Science Indicators ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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