  Generative Adversarial Networks (GANs) are a recent advancement in
unsupervised machine learning. They are a cat-and-mouse game between two neural
networks: [1] a discriminator network which learns to validate whether a sample
is real or fake compared to a training set and [2] a generator network which
learns to generate data that appear to belong to the training set. Both
networks learn from each other until training is complete and the generator
network is able to produce samples that are indistinguishable from the training
set. We find that GANs are well-suited for fast generation of novel 3D density
maps that are indistinguishable from those obtained from N-body simulations. In
a matter of seconds, a fully trained GAN can generate thousands of density maps
at different epochs in the history of the universe. These GAN-generated maps
can then be used to study the evolution of large-scale structure over time.
