Predicting large scale cosmological structure evolution with GAN-based
  autoencoders
Marion Ullmo Nabila Aghnim Aurélien Decelle Miguel Aragon-Calvo
Cosmological simulations play a key role in the prediction and understanding
of large scale structure formation from initial conditions. We make use of
GAN-based Autoencoders (AEs) in an attempt to predict structure evolution
within simulations. The AEs are trained on images and cubes issued from
respectively 2D and 3D N-body simulations describing the evolution of the dark
matter (DM) field. We find that while the AEs can predict structure evolution
for 2D simulations of DM fields well, using only the density fields as input,
they perform significantly more poorly in similar conditions for 3D
simulations. However, additionally providing velocity fields as inputs greatly
improves results, with similar predictions regardless of time-difference
between input and target.