Open AccessUsing Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations
Author(s) -
Matthew Li,
Christopher McComb
Publication year - 2022
Publication title -
journal of computing and information science in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.538
H-Index - 50
eISSN - 1944-7078
pISSN - 1530-9827
DOI - 10.1115/1.4053671
Subject(s) - context (archaeology) , computational fluid dynamics , computer science , fluid dynamics , turbulence , multiphase flow , upsampling , artificial intelligence , computational science , algorithm , machine learning , image (mathematics) , mechanics , physics , paleontology , biology
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