High-fidelity reconstruction of large-area damaged turbulent fields with a physically constrained generative adversarial network

In this work, we propose a novel framework for the high-fidelity reconstruction of large-area damaged turbulent fields with high resolution based on a physically constrained generative adversarial network. The network leverages complete/sparse fields of velocity components as physical constraints and adopts a PatchGAN discriminator network. The proposed reconstruction framework has been shown to achieve excellent reconstruction performance. The reconstructed flow fields are consistent with the raw flow fields in terms of magnitude, power spectrum, and two-point correlation function.