Super-resolution emulator of cosmological simulations using deep
  physical models
  We present an extension of our recently developed Wasserstein optimized model
to emulate accurate high-resolution features from computationally cheaper
low-resolution cosmological simulations. Our deep physical modelling technique
relies on restricted neural networks to perform a mapping of the distribution
of the low-resolution cosmic density field to the space of the high-resolution
small-scale structures. We constrain our network using a single triplet of
high-resolution initial conditions and the corresponding low- and
high-resolution evolved dark matter simulations from the Quijote suite of
simulations. We exploit the information content of the high-resolution initial
conditions as a well constructed prior distribution from which the network
emulates the small-scale structures. Once fitted, our physical model yields
emulated high-resolution simulations at low computational cost, while also
providing some insights about how the large-scale modes affect the small-scale
structure in real space.
