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 {\tt stacked U-net}, can capture the main features of the non-linear evolution of hydrodynamics, which could also rapidly predict the final profiles for various testing initial conditions.

arXiv commentQM2018 proceeding, 4 pages, 4 figures

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• Collective Effects in Nuclear Collisions: Experimental Overview
• Classifying Topological Charge in SU(3) Yang-Mills Theory with Machine Learning
• Hydrodynamical response of plane correlation in Pb+Pb collisions at $\sqrt{s_{\text{NN}}}$=2.76 TeV
• Complete CVDL Methodology for Investigating Hydrodynamic Instabilities