Abstract
Abstract
Highlights
In recent years, machine learning methods have been utilized to tackle various problems in fluid dynamics (Brenner, Eldredge & Freund 2019; Brunton, Hemanti & Taira 2020a; Fukami, Fukagata & Taira 2020a; Brunton, Noack & Koumoutsakos 2020b)
We recently proposed a super resolution (SR) reconstruction method for fluid flows, which was tested for two-dimensional laminar cylinder wake and two-dimensional decaying homogeneous isotropic turbulence (Fukami, Fukagata & Taira 2019a)
We demonstrated that high-resolution two-dimensional turbulent flow fields of a 128 × 128 grid can be reconstructed from the input data on a coarse 4 × 4 grid via machine learning methods
Summary
Machine learning methods have been utilized to tackle various problems in fluid dynamics (Brenner, Eldredge & Freund 2019; Brunton, Hemanti & Taira 2020a; Fukami, Fukagata & Taira 2020a; Brunton, Noack & Koumoutsakos 2020b). Applications of machine learning for turbulence modelling have been active in fluid dynamics (Kutz 2017; Duraisamy, Iaccarino & Xiao 2019). For large-eddy simulation, subgrid modelling assisted by machine learning was proposed by Maulik et al (2019). They showed the capability of machine learning assisted subgrid modeling in a priori and a posteriori tests for the Kraichnan turbulence
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