  We propose a supervised-machine-learning-based wall model for coarse-grid
wall-resolved large-eddy simulation (LES). Our consideration is made on LES of
turbulent channel flows with a first grid point set relatively far from the
wall ($\sim$ 10 wall units), while still resolving the near-wall region, to
present a new path to save the computational cost. Convolutional neural network
(CNN) is utilized to estimate a virtual wall-surface velocity from $x-z$
sectional fields near the wall, whose training data are generated by a direct
numerical simulation (DNS) at ${\rm Re}_{\tau}=180$. The virtual wall-surface
velocity is prepared with the extrapolation of the DNS data near the wall. This
idea enables us to give a proper wall condition to correct a velocity gradient
near the wall. The estimation ability of the model from near wall information
is first investigated as a priori test. The estimated velocity fields by the
present CNN model are in statistical agreement with the reference DNS data. The
model trained in a priori test is then combined with the LES as a posteriori
test. We find that the LES can successfully be augmented using the present
model at both the friction Reynolds number ${\rm Re}_{\tau}=180$ used for
training and the unseen Reynolds number ${\rm Re}_{\tau}=360$ even when the
first grid point is located at 5 wall units off the wall. We also investigate
the robustness of the present model for the choice of sub-grid scale model and
the possibility of transfer learning in a local domain. The observations
through the paper suggest that the present model is a promising tool for
recovering the accuracy of LES with a coarse grid near the wall.
