Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity. (arXiv:2112.08676v2 [cs.LG] UPDATED)

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 …

application arxiv computational elasticity linear machine machine learning solid