  Modelling the near-wall region of wall-bounded turbulent flows is a
widespread practice to reduce the computational cost of large-eddy simulations
(LESs) at high Reynolds number. As a first step towards a data-driven
wall-model, a neural-network-based approach to predict the near-wall behaviour
in a turbulent open channel flow is investigated. The fully-convolutional
network (FCN) proposed by Guastoni et al. [preprint, arXiv:2006.12483] is
trained to predict the two-dimensional velocity-fluctuation fields at
$y^{+}_{\rm target}$, using the sampled fluctuations in wall-parallel planes
located farther from the wall, at $y^{+}_{\rm input}$. The data for training
and testing is obtained from a direct numerical simulation (DNS) at friction
Reynolds numbers $Re_{\tau} = 180$ and $550$. The turbulent
velocity-fluctuation fields are sampled at various wall-normal locations, i.e.
$y^{+} = \{15, 30, 50, 80, 100, 120, 150\}$. At $Re_{\tau}=550$, the FCN can
take advantage of the self-similarity in the logarithmic region of the flow and
predict the velocity-fluctuation fields at $y^{+} = 50$ using the
velocity-fluctuation fields at $y^{+} = 100$ as input with less than 20% error
in prediction of streamwise-fluctuations intensity. These results are an
encouraging starting point to develop a neural-network based approach for
modelling turbulence at the wall in numerical simulations.
