Neural ordinary differential equation and holographic quantum chromodynamics
Neural ordinary differential equation and holographic quantum chromodynamics
复制标题
神经常微分方程和全息量子色动力学
DOI：
10.1088/2632-2153/abe527
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Yi
中科院分区：
文献类型：
--
作者：
K. Hashimoto;Hong;Yi
神经元常微分方程是一种新型的机器学习结构，其权值是连续深度的光滑函数。我们把权函数看作体度规，将神经常微分方程应用于全息QCD，并用有限温度下手征凝聚体的格点QCD数据训练神经常微分方程。这台机器在不同的温度值下发现一致的体几何，并自动发现全息体中出现的黑洞视界。全息威尔逊环计算与涌现机器学习体时空具有一致的温度依赖性的限制和德拜屏蔽行为。在具有物理可解释权重的机器学习模型中，神经ODE使我们摆脱了导致超参数难以巧妙处理的离散化伪影，并提高了数值精度，使模型更值得信赖。
The neural ordinary differential equation (neural ODE) is a novel machine learning architecture whose weights are smooth functions of the continuous depth. We apply the neural ODE to holographic QCD by regarding the weight functions as a bulk metric, and train the machine with lattice QCD data of chiral condensate at finite temperature. The machine finds consistent bulk geometry at various values of temperature and discovers the emergent black hole horizon in the holographic bulk automatically. The holographic Wilson loops calculated with the emergent machine-learned bulk spacetime have consistent temperature dependence of confinement and Debye-screening behavior. In machine learning models with physically interpretable weights, the neural ODE frees us from discretization artifact leading to difficult ingenuity of hyperparameters, and improves numerical accuracy to make the model more trustworthy.