StyleGAN as a Deconvolutional Operator for Large Eddy Simulations

We present a novel deconvolution operator for Large Eddy Simulation (LES) of turbulent flows based on the latest StyleGAN deep learning networks. We exploit the flexibility of this architecture in separating the different layers of the GAN generator, which can be seen as instantaneous fields of the LES. These can be moved in time via integrating the corresponding filtered Navier-Stokes (NS) equations. The subgrid-scale (SGS) stress tensor is obtained from the reconstructed field, rather than ad-hoc turbulence models. We trained a StyleGAN-based network (MSG-StyleGAN) with 5000 images of a decaying 2D-Homogeneous Isotropic Turbulence (2D-HIT) starting at Re-lambda = 60 using a 256x256 grid mesh size. We then reconstructed a DNS simulation, point by point, using a 32x32 resolution via research into the latent space of the GAN until the difference between internal fields and LES fields are within a given tolerance. Results show convergence towards the ground truth DNS solution as the tolerance approaches zero.