  Data-driven methods for modelling purposes in fluid mechanics are a promising
alternative given the continuous increase of both computational power and
data-storage capabilities. Highly non-linear flows including turbulence and
reaction are challenging to model, and accurate closures for the unresolved
terms in large eddy simulations of such flows are difficult to obtain. In this
study, we investigate the use of artificial neural networks for modelling an
important unclosed term namely the unresolved stress tensor, in a highly
demanding turbulent and reacting flow, which additionally includes mean shear.
The performance of the neural network-based modelling approach is conducted a
priori following a coarsened mesh approach, and compared against the
predictions of eight other classic models in the literature, which include both
static and dynamic formulations.
