  Despite the increasing availability of high-performance computational
resources, Reynolds-Averaged Navier-Stokes (RANS) simulations remain the
workhorse for the analysis of turbulent flows in real-world applications.
Linear eddy viscosity models (LEVM), the most commonly employed model type,
cannot accurately predict complex states of turbulence. This work combines a
deep-neural-network-based, nonlinear eddy viscosity model with turbulence
realizability constraints as an inductive bias in order to yield improved
predictions of the anisotropy tensor. Using visualizations based on the
barycentric map, we show that the proposed machine learning method's anisotropy
tensor predictions offer a significant improvement over all LEVMs in
traditionally challenging cases with surface curvature and flow separation.
However, this improved anisotropy tensor does not, in general, yield improved
mean-velocity and pressure field predictions in comparison with the
best-performing LEVM.
