  Supervised super-resolution deep convolutional neural networks (CNNs) have
gained significant attention for their potential in reconstructing velocity and
scalar fields in turbulent flows. Despite their popularity, CNNs currently lack
the ability to accurately produce high-frequency and small-scale features, and
tests of their generalizability to out-of-sample flows are not widespread.
Generative adversarial networks (GANs), which consist of two distinct neural
networks (NNs), a generator and discriminator, are a promising alternative,
allowing for both semi-supervised and unsupervised training. The difference in
the flow fields produced by these two NN architectures has not been thoroughly
investigated, and a comprehensive understanding of the discriminator's role has
yet to be developed. This study assesses the effectiveness of the unsupervised
adversarial training in GANs for turbulence reconstruction in forced
homogeneous isotropic turbulence. GAN-based architectures are found to
outperform supervised CNNs for turbulent flow reconstruction for in-sample
cases. The reconstruction accuracy of both architectures diminishes for
out-of-sample cases, though the GAN's discriminator network significantly
improves the generator's out-of-sample robustness using either an additional
unsupervised training step with large eddy simulation input fields and a
dynamic selection of the most suitable upsampling factor. These enhance the
generator's ability to reconstruct small-scale gradients, turbulence
intermittency, and velocity-gradient probability density functions. The
extrapolation capability of the GAN-based model is demonstrated for
out-of-sample flows at higher Reynolds numbers. Based on these findings,
incorporating discriminator-based training is recommended to enhance the
reconstruction capability of super-resolution CNNs.
