  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.
