arXiv 2006.007122020-06-01hep-thcond-mat.dis-nngr-qchep-ph

神经ODE和全息QCD

Neural ODE and Holographic QCD

Koji Hashimoto, Hong-Ye Hu, Yi-Zhuang You

备注：: 10 pages, 6 figures
发表信息：: Mach. Learn.: Sci. Technol. 2 035011 (2021)
DOI：: 10.1088/2632-2153/abe527

摘要：

神经常微分方程（Neural ODE）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。将权函数看作体度规，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量子色动力学数据对神经常微分方程进行训练。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。用涌现机器学习体时空计算的全息威尔逊圈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创造性的离散化伪影，并提高了数值精度，使模型更值得信赖。

英文摘要：

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.

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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