arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1801.03334nucl-thastro-ph.HEcond-mat.dis-nnhep-ph
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深度学习在相对论流体力学中的应用

Applications of deep learning to relativistic hydrodynamics

Hengfeng Huang, Bowen Xiao, Ziming Liu, Zeming Wu, Yadong Mu, Huichao Song

摘要:

相对论流体力学是模拟相对论重离子碰撞中夸克-胶子等离子体离子体演化的有力工具。利用2+1-d相对论流体动力学VISH 2 +1在MC-Glauber初始条件下生成的10000个初始和最终剖面,我们训练了一个基于堆叠U-网的深度神经网络,并使用它来预测与各种初始条件相关的最终剖面,包括MC-Glauber,MC-KLN和AMPT和TRENTo。与VISH 2 +1结果的比较表明,该网络预测能很好地捕捉到最终剖面的震级和非均匀结构,并能很好地描述相关的偏心率分布$P(\varepsilon_n)$(n=2,3,4)。这些结果表明,深度学习技术可以捕捉流体力学非线性演化的主要特征,显示出其在很大程度上加速相对论流体力学逐事件模拟的潜力。

英文摘要:

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 $P(\varepsilon_n)$ (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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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