  State-of-the-art galaxy formation simulations generate data within weeks or
months. Their results consist of a random sub-sample of possible galaxies with
a fixed number of stars. We propose a ML based method, GalacticFlow, that
generalizes such results. We use normalizing flows to learn the extended
distribution function of galaxies conditioned on global galactic parameters.
GalacticFlow then provides a continuized and condensed representation of the
ensemble of galaxies in the data. Thus, essentially compressing large amounts
of explicit simulation data into a small implicit generative model. Our model
is able to evaluate any galaxy eDF given by a set of global parameters and
allows generating arbitrarily many stars from it. We show that we can learn
such a representation, embodying the entire mass range from dwarf to Milky Way
mass, from only 90 galaxies in $\sim18$ hours on a single RTX 2080Ti and
generate a new galaxy of one million stars within a few seconds.
