  The objective of this study is to assess the capability of convolution-based
neural networks to predict wall quantities in a turbulent open channel flow.
The first tests are performed by training a fully-convolutional network (FCN)
to predict the 2D velocity-fluctuation fields at the inner-scaled wall-normal
location $y^{+}_{\rm target}$, using the sampled velocity fluctuations in
wall-parallel planes located farther from the wall, at $y^{+}_{\rm input}$. The
predictions from the FCN are compared against the predictions from a proposed
R-Net architecture. Since the R-Net model is found to perform better than the
FCN model, the former architecture is optimized to predict the 2D streamwise
and spanwise wall-shear-stress components and the wall pressure from the
sampled velocity-fluctuation fields farther from the wall. The dataset is
obtained from DNS of open channel flow at $Re_{\tau} = 180$ and $550$. The
turbulent velocity-fluctuation fields are sampled at various inner-scaled
wall-normal locations, along with the wall-shear stress and the wall pressure.
At $Re_{\tau}=550$, both FCN and R-Net 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 about 10% error in prediction of
streamwise-fluctuations intensity. Further, the R-Net is also able to predict
the wall-shear-stress and wall-pressure fields using the velocity-fluctuation
fields at $y^+ = 50$ with around 10% error in the intensity of the
corresponding fluctuations at both $Re_{\tau} = 180$ and $550$. These results
are an encouraging starting point to develop neural-network-based approaches
for modelling turbulence near the wall in large-eddy simulations.
