  Cosmologists aim to model the evolution of initially low amplitude Gaussian
density fluctuations into the highly non-linear "cosmic web" of galaxies and
clusters. They aim to compare simulations of this structure formation process
with observations of large-scale structure traced by galaxies and infer the
properties of the dark energy and dark matter that make up 95% of the universe.
These ensembles of simulations of billions of galaxies are computationally
demanding, so that more efficient approaches to tracing the non-linear growth
of structure are needed. We build a V-Net based model that transforms fast
linear predictions into fully nonlinear predictions from numerical simulations.
Our NN model learns to emulate the simulations down to small scales and is both
faster and more accurate than the current state-of-the-art approximate methods.
It also achieves comparable accuracy when tested on universes of significantly
different cosmological parameters from the one used in training. This suggests
that our model generalizes well beyond our training set.
