Dark matter in the universe evolves through gravity to form a complex network
of halos, filaments, sheets and voids, that is known as the cosmic web.
Computational models of the underlying physical processes, such as classical
N-body simulations, are extremely resource intensive, as they track the action
of gravity in an expanding universe using billions of particles as tracers of
the cosmic matter distribution. Therefore, upcoming cosmology experiments will
face a computational bottleneck that may limit the exploitation of their full
scientific potential. To address this challenge, we demonstrate the application
of a machine learning technique called Generative Adversarial Networks (GAN) to
learn models that can efficiently generate new, physically realistic
realizations of the cosmic web. Our training set is a small, representative
sample of 2D image snapshots from N-body simulations of size 500 and 100 Mpc.
We show that the GAN-produced results are qualitatively and quantitatively very
similar to the originals. Generation of a new cosmic web realization with a GAN
takes a fraction of a second, compared to the many hours needed by the N-body
technique. We anticipate that GANs will therefore play an important role in
providing extremely fast and precise simulations of cosmic web in the era of
large cosmological surveys, such as Euclid and LSST.