  Artificial neural network (ANN) is tested as a tool for finding a new subgrid
model of the subgrid-scale (SGS) stress in large-eddy simulation. ANN is used
to establish a functional relation between the grid-scale (GS) flow field and
the SGS stress without any assumption of the form of function. Data required
for training and test of ANN are provided by direct numerical simulation (DNS)
of a turbulent channel flow. It is shown that ANN can establish a model similar
to the gradient model. The correlation coefficients between the real SGS stress
and the output of ANN are comparable to or larger than similarity models, but
smaller than a two-parameter dynamic mixed model.
