  Recently a type of neural networks called Generative Adversarial Networks
(GANs) has been proposed as a solution for fast generation of simulation-like
datasets, in an attempt to bypass heavy computations and expensive cosmological
simulations to run in terms of time and computing power. In the present work,
we build and train a GAN to look further into the strengths and limitations of
such an approach. We then propose a novel method in which we make use of a
trained GAN to construct a simple autoencoder (AE) as a first step towards
building a predictive model. Both the GAN and AE are trained on images issued
from two types of N-body simulations, namely 2D and 3D simulations. We find
that the GAN successfully generates new images that are statistically
consistent with the images it was trained on. We then show that the AE manages
to efficiently extract information from simulation images, satisfyingly
inferring the latent encoding of the GAN to generate an image with similar
large scale structures.
