Thursday·January 11, 2018

[Submitted on 10 Jan 2018]

Applications of deep learning to relativistic hydrodynamics

Hengfeng Huang🇨🇳 · Bowen Xiao🇨🇳 · Ziming Liu🇨🇳 · Zeming Wu🇨🇳 · Yadong Mu🇨🇳 · Huichao Song🇨🇳

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 $P({\epsilon }_n)$ (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.