  Neural-network models have been employed to predict the instantaneous flow
close to the wall in a viscoelastic turbulent channel flow. The numerical
simulation data at the wall is utilized to predict the instantaneous velocity
fluctuations and polymeric-stress fluctuations at three different wall-normal
positions. Apart from predicting the velocity fluctuations well in a
hibernating flow, the neural-network models are also shown to predict the
polymeric shear stress and the trace of the polymeric stresses at a given
wall-normal location with reasonably good accuracy. These non-intrusive sensing
models can be integrated in an experimental setting to construct the
polymeric-stress field in turbulent flows, which otherwise may not be directly
quantifiable in experimental measurements.
