论文标题
神经和全息QCD
Neural ODE and Holographic QCD
论文作者
论文摘要
神经普通微分方程（神经颂）是一种新型的机器学习结构，其权重是连续深度的平滑函数。我们将重量作为批量度量的功能应用于全息QCD，并在有限温度下用手性冷凝物的晶格QCD数据训练机器。该机器在温度的各种值下找到一致的散装几何形状，并自动发现全息散装中的新兴黑洞地平线。用紧急机器学习的散装时段计算出的全息循环具有一致的限制温度依赖性和debye筛选行为。在具有物理解释权重的机器学习模型中，神经颂歌使我们摆脱了离散化的人工制作，导致超参数的艰难创造力，并提高了数值准确性，以使模型更加值得信赖。
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.
