  This work presents a novel physics-informed deep learning based
super-resolution framework to reconstruct high-resolution deformation fields
from low-resolution counterparts, obtained from coarse mesh simulations or
experiments. We leverage the governing equations and boundary conditions of the
physical system to train the model without using any high-resolution labeled
data. The proposed approach is applied to obtain the super-resolved deformation
fields from the low-resolution stress and displacement fields obtained by
running simulations on a coarse mesh for a body undergoing linear elastic
deformation. We demonstrate that the super-resolved fields match the accuracy
of an advanced numerical solver running at 400 times the coarse mesh
resolution, while simultaneously satisfying the governing laws. A brief
evaluation study comparing the performance of two deep learning based
super-resolution architectures is also presented.
