  Traditionally, incorporating additional physics into existing cosmological
simulations requires re-running the cosmological simulation code, which can be
computationally expensive. We show that conditional Generative Adversarial
Networks (cGANs) can be harnessed to predict how changing the underlying
physics alters the simulation results. To illustrate this, we train a cGAN to
learn the impact of dark matter annihilation feedback (DMAF) on the gas density
distribution. The predicted gas density slices are visually difficult to
distinguish from their real brethren and the peak counts differ by less than 10
per cent for all test samples (the average deviation is < 3 per cent). Finally,
we invert the problem and show that cGANs are capable of endowing smooth
density distributions with realistic substructure. The cGAN does however have
difficulty generating new knots as well as creating/eliminating bubble-like
structures. We conclude that trained cGANs can be an effective approach to
provide mock samples of cosmological simulations incorporating DMAF physics
from existing samples of standard cosmological simulations of the evolution of
cosmic structure.
