StyleGAN as an AI deconvolution operator for large eddy simulations of turbulent plasma equations in BOUT++

Abstract

We present the use of StyleGAN, a face-synthesis generative adversarial network (GAN) developed by NVidia, as a deconvolution operator for large eddy simulation (LES) of plasma turbulence. The overall methodology, named style eddy simulation, has been integrated into the BOUT++ solver and tested on the original and modified Hasegawa–Wakatani models using different mesh sizes, 2562 and 5122, and different values of the adiabaticity parameter α and background density gradient κ. Using a LES resolution of 32 × 32 and 64 × 64, i.e., 64× smaller resolution than the corresponding direct numerical simulation (DNS), results show convergence toward the ground truth as we tighten the reconstruction tolerance, and an algorithm complexity O(N log N) is compared to the O(N2) of BOUT++. Finally, the trained GAN can be used to create valid initial conditions for a faster DNS by avoiding to start from nonphysical initial perturbations.