  Relativistic hydrodynamics is a powerful tool to simulate the evolution of
the quark gluon plasma (QGP) in relativistic heavy ion collisions. Using 10000
initial and final profiles generated from 2+1-d relativistic hydrodynamics
VISH2+1 with MC-Glauber initial conditions, we train a deep neural network
based on stacked U-net, and use it to predict the final profiles associated
with various initial conditions, including MC-Glauber, MC-KLN and AMPT and
TRENTo. A comparison with the VISH2+1 results shows that the network
predictions can nicely capture the magnitude and inhomogeneous structures of
the final profiles, and nicely describe the related eccentricity distributions
$P(\varepsilon_n)$ (n=2, 3, 4). These results indicate that deep learning
technique can capture the main features of the non-linear evolution of
hydrodynamics, showing its potential to largely accelerate the event-by-event
simulations of relativistic hydrodynamics.
