  We demonstrate how deep convolutional neural networks can be trained to
predict 2+1 D hydrodynamic simulation results for flow coefficients,
mean-transverse-momentum and charged particle multiplicity from the initial
energy density profile. We show that this method provides results that are
accurate enough, so that one can use neural networks to reliably estimate
multi-particle flow correlators. Additionally, we train networks that can take
any model parameter as an additional input and demonstrate with a few examples
that the accuracy remains good. The usage of neural networks can reduce the
computation time needed in performing Bayesian analyses with multi-particle
flow correlators by many orders of magnitude.
