  Generalizability of machine-learning (ML) assisted turbulence closure models
to unseen flows remains an important challenge. It is well known that the
architecture of a neural network and the manner of training have a profound
influence on the predictive capability and generalizability of the resulting
model [I. Goodfellow, Y. Bengio, and A. Courville, Deep learning, Vol. 1
(2016)]. The objective of the present work is to characterize the influence of
the choice of (i) the network (in terms of the number of neurons and hidden
layers); and (ii) training process elements such as type of loss function on
the generalizability of the ML models. A systematic examination of ML model
generalizability over a broad parameter regime requires detailed datasets for
many flows in the entire domain. The main novelty of the present study is to
utilize a simplified proxy-physics surrogate of turbulence to obtain data
rather than prohibitively expensive high-fidelity simulations or experiments.
The chosen proxy model exhibits important features including bifurcation and
rapid distortion effects in different parameter regimes. Using proxy-physics
data, training and testing of various neural networks are performed over
different regimes. Optimal network architecture and training techniques are
identified based on performance in these test studies. The findings provide a
clear basis for choosing neural network architecture and training procedure for
turbulence applications.
