  This work presents a physics-informed neural network (PINN) based framework
to model the strain-rate and temperature dependence of the deformation fields
in elastic-viscoplastic solids. To avoid unbalanced back-propagated gradients
during training, the proposed framework uses a simple strategy with no added
computational complexity for selecting scalar weights that balance the
interplay between different terms in the physics-based loss function. In
addition, we highlight a fundamental challenge involving the selection of
appropriate model outputs so that the mechanical problem can be faithfully
solved using a PINN-based approach. We demonstrate the effectiveness of this
approach by studying two test problems modeling the elastic-viscoplastic
deformation in solids at different strain rates and temperatures, respectively.
Our results show that the proposed PINN-based approach can accurately predict
the spatio-temporal evolution of deformation in elastic-viscoplastic materials.
