# Quark Matter 2018

13-19 May 2018
Venice, Italy
Europe/Zurich timezone
The organisers warmly thank all participants for such a lively QM2018! See you in China in 2019!

## Applications of deep learning in relativistic hydrodynamics

16 May 2018, 15:00
20m
Sala Mosaici-1, 3rd Floor (Palazzo del Casinò)

### Sala Mosaici-1, 3rd Floor

#### Palazzo del Casinò

Parallel Talk New theoretical developments

### Speaker

Hengfeng Huang (Peking University)

### Description

Deep learning is one of the machine learning technologies developed in computer science. Recently, it has been implemented to various research areas in physics, including search of gravitational lens [1], identifying and classifying the phases of Ising model [2], the search of Higgs and exotic particles [3], classification jet structure [4], etc. In this talk, we will implement deep learning to relativistic hydrodynamics, which is a useful tool to simulate the evolution of relativistic systems in high energy nuclear physics and astrophysics [5].

Using 10000 initial and final energy density and flow velocity profiles generated from 2+1-d hydrodynamics with MC-Glauber initial conditions, we train the network 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 hydrodynamic calculations shows that the network predictions can nicely capture the magnitude and inhomogeneous structures of these final profiles, as well as the related eccentricity distributions $P(\varepsilon_n)$ (n=2, 3, 4). These results indicate that deep learning can capture the main feature of the non-linear evolution of hydrodynamics, which shows the potential of largely accelerate the simulations of relativistic hydrodynamics.

Reference
[1] Y. D. Hezaveh, et.al., Nature 548, 555 (2017).
[2] J. Carrasquilla and G. R. Melko, Nature Phys. 13, 431 (2017).
[3] P. Baldi, et.al, Nature Commun. 5, 4308 (2014).
[4] P. T. Komiske, et. al., JHEP 1701, 110 (2017).
[5] H. Huang, B. Xiao, H. Xiong, Z. Wu, Y. Mu and H. Song, in preparation.

Content type Theory Presenter name already specified

### Primary author

Hengfeng Huang (Peking University)