  We extend the physics-informed neural network (PINN) method to learn
viscosity models of two non-Newtonian systems (polymer melts and suspensions of
particles) using only velocity measurements. The PINN-inferred viscosity models
agree with the empirical models for shear rates with large absolute values but
deviate for shear rates near zero where the analytical models have an
unphysical singularity. Once a viscosity model is learned, we use the PINN
method to solve the momentum conservation equation for non-Newtonian fluid flow
using only the boundary conditions.
