Applications of deep learning to relativistic hydrodynamics

Abstract In this proceeding, we will briefly review our recent progress on implementing deep learning to relativistic hydrodynamics. We will demonstrate that a successfully designed and trained deep neural network, called stacked U-net , can capture the main features of the non-linear evolution of hydrodynamics, which also rapidly predicts the final profiles for various testing initial conditions.