Contribution to a conference proceedings/Contribution to a book

FZJ-2023-00624

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Large-Eddy Simulations of ECN Spray C

Bode, M. (Corresponding author)FZJ*

2022

Please use a persistent id in citations: http://hdl.handle.net/2128/33600

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Contributing Institute(s):

1. 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 2022

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Database coverage:

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