Applications of deep learning to relativistic hydrodynamics
Nuclear Theory
2019-02-20 v1 Disordered Systems and Neural Networks
High Energy Physics - Experiment
High Energy Physics - Phenomenology
Nuclear Experiment
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.
Cite
@article{arxiv.1807.05728,
title = {Applications of deep learning to relativistic hydrodynamics},
author = {Hengfeng Huang and Bowen Xiao and Huixin Xiong and Zeming Wu and Yadong Mu and Huichao Song},
journal= {arXiv preprint arXiv:1807.05728},
year = {2019}
}
Comments
QM2018 proceeding, 4 pages, 4 figures