  Viscoelastic fluids are a class of fluids that exhibit both viscous and
elastic nature. Modelling such fluids requires constitutive equations for the
stress, and choosing the most appropriate constitutive relationship can be
difficult. We present viscoelasticNet, a physics-informed deep learning
framework that uses the velocity flow field to select the constitutive model
and learn the stress field. Our framework requires data only for the velocity
field, initial \& boundary conditions for the stress tensor, and the boundary
condition for the pressure field. Using this information, we learn the model
parameters, the pressure field, and the stress tensor. {This work considers}
three commonly used non-linear viscoelastic models: Oldroyd-B, Giesekus, and
linear Phan-Thien-Tanner (PTT). We demonstrate that our framework works well
with noisy and sparse data. Our framework can be combined with velocity fields
acquired from experimental techniques like particle image velocimetry to get
the pressure \& stress fields and model parameters for the constitutive
equation. Once the model has been discovered using viscoelasticNet, the fluid
can be simulated and modeled for further applications.
