  In recent years, the evolution of artificial intelligence, especially deep
learning, has been remarkable, and its application to various fields has been
growing rapidly. In this paper, I report the results of the application of
generative adversarial networks (GANs), specifically video-to-video translation
networks, to computational fluid dynamics (CFD) simulations. The purpose of
this research is to reduce the computational cost of CFD simulations with GANs.
The architecture of GANs in this research is a combination of the
image-to-image translation networks (the so-called "pix2pix") and Long
Short-Term Memory (LSTM). It is shown that the results of high-cost and
high-accuracy simulations (with high-resolution computational grids) can be
estimated from those of low-cost and low-accuracy simulations (with
low-resolution grids). In particular, the time evolution of density
distributions in the cases of a high-resolution grid is reproduced from that in
the cases of a low-resolution grid through GANs, and the density inhomogeneity
estimated from the image generated by GANs recovers the ground truth with good
accuracy. Qualitative and quantitative comparisons of the results of the
proposed method with those of several super-resolution algorithms are also
presented.
