  In this work, we provide an efficient and realistic data-driven approach to
simulate astronomical images using deep generative models from machine
learning. Our solution is based on a variant of the generative adversarial
network (GAN) with progressive training methodology and Wasserstein cost
function. The proposed solution generates naturalistic images of galaxies that
show complex structures and high diversity, which suggests that data-driven
simulations using machine learning can replace many of the expensive
model-driven methods used in astronomical data processing.
