835 citations · 3.7k across the 23 of their papers we have counts for
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physics.flu-dyn2018★ 835 cited
Super-resolution reconstruction of turbulent flows with machine learning
Kai Fukami, Koji Fukagata, Kunihiko Taira
We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field. Two machine-learning…
physics.flu-dyn2018★ 144 cited
Synthetic turbulent inflow generator using machine learning
Kai Fukami, Yusuke Nabae, Ken Kawai +1
We propose a methodology for generating time-dependent turbulent inflow data with the aid of machine learning (ML), which has a possibility to replace conventional driver simulatio…