  Generative adversarial networks (GANs) have been recently applied as a novel
emulation technique for large scale structure simulations. Recent results show
that GANs can be used as a fast, efficient and computationally cheap emulator
for producing novel weak lensing convergence maps as well as cosmic web data in
2-D and 3-D. However, like any algorithm, the GAN approach comes with a set of
limitations, such as an unstable training procedure and the inherent randomness
of the produced outputs. In this work we employ a number of techniques commonly
used in the machine learning literature to address the mentioned limitations.
In particular, we train a GAN to produce both weak lensing convergence maps and
dark matter overdensity field data for multiple redshifts, cosmological
parameters and modified gravity models. In addition, we train a GAN using the
newest Illustris data to emulate dark matter, gas and internal energy
distribution data simultaneously. Finally, we apply the technique of latent
space interpolation to control which outputs the algorithm produces. Our
results indicate a 1-20% difference between the power spectra of the
GAN-produced and the training data samples depending on the dataset used and
whether Gaussian smoothing was applied. Finally, recent research on generative
models suggests that such algorithms can be treated as mappings from a
lower-dimensional input (latent) space to a higher dimensional (data) manifold.
We explore such a theoretical description as a tool for better understanding
the latent space interpolation procedure.
