  The Reynolds-averaged Navier-Stokes (RANS) equations are widely used in
turbulence applications. They require accurately modeling the anisotropic
Reynolds stress tensor, for which traditional Reynolds stress closure models
only yield reliable results in some flow configurations. In the last few years,
there has been a surge of work aiming at using data-driven approaches to tackle
this problem. The majority of previous work has focused on the development of
fully-connected networks for modeling the anisotropic Reynolds stress tensor.
In this paper, we expand upon recent work for turbulent channel flow and
develop new convolutional neural network (CNN) models that are able to
accurately predict the normalized anisotropic Reynolds stress tensor. We apply
the new CNN model to a number of one-dimensional turbulent flows. Additionally,
we present interpretability techniques that help drive the model design and
provide guidance on the model behavior in relation to the underlying physics.
