A GAN trained on DNS data at three Reynolds numbers generates statistically realistic turbulent channel flow at intermediate Reynolds numbers, and the RNN-GAN extension produces long time series with good spatiotemporal correlations.
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Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers
A GAN trained on DNS data at three Reynolds numbers generates statistically realistic turbulent channel flow at intermediate Reynolds numbers, and the RNN-GAN extension produces long time series with good spatiotemporal correlations.