  Measuring the sum of the three active neutrino masses, $M_\nu$, is one of the
most important challenges in modern cosmology. Massive neutrinos imprint
characteristic signatures on several cosmological observables in particular on
the large-scale structure of the Universe. In order to maximize the information
that can be retrieved from galaxy surveys, accurate theoretical predictions in
the non-linear regime are needed. Currently, one way to achieve those
predictions is by running cosmological numerical simulations. Unfortunately,
producing those simulations requires high computational resources -- several
hundred to thousand core-hours for each neutrino mass case. In this work, we
propose a new method, based on a deep learning network, to quickly generate
simulations with massive neutrinos from standard $\Lambda$CDM simulations
without neutrinos. We computed multiple relevant statistical measures of
deep-learning generated simulations, and conclude that our approach is an
accurate alternative to the traditional N-body techniques. In particular the
power spectrum is within $\simeq 6\%$ down to non-linear scales $k=0.7$~\rm
h/Mpc. Finally, our method allows us to generate massive neutrino simulations
10,000 times faster than the traditional methods.
