  Computational Fluid Dynamics (CFD) is used in the design and optimization of
gas turbines and many other industrial/ scientific applications. However, the
practical use is often limited by the high computational cost, and the accurate
resolution of near-wall flow is a significant contributor to this cost. Machine
learning (ML) and other data-driven methods can complement existing wall
models. Nevertheless, training these models is bottlenecked by the large
computational effort and memory footprint demanded by back-propagation. Recent
work has presented alternatives for computing gradients of neural networks
where a separate forward and backward sweep is not needed and storage of
intermediate results between sweeps is not required because an unbiased
estimator for the gradient is computed in a single forward sweep. In this
paper, we discuss the application of this approach for training a subgrid wall
model that could potentially be used as a surrogate in wall-bounded flow CFD
simulations to reduce the computational overhead while preserving predictive
accuracy.
