  Due to the limit of mesh density, the improvement of the spatial precision of
numerical computation always leads to a decrease in computing efficiency.
Aiming at this inability of numerical computation, we propose a novel method
for boosting the mesh density in numerical computation within the 2D domain.
Based on the low mesh-density stress field in the 2D plane strain problem
computed by the finite element method, this method utilizes a deep neural
network named SuperMeshingNet to learn the non-linear mapping from low
mesh-density to high mesh-density stress field, and realizes the improvement of
numerical computation accuracy and efficiency simultaneously. We adopt residual
dense blocks to our mesh-density boost model called SuperMeshingNet for
extracting abundant local features and enhancing the prediction capacity of the
model. Experimental results show that the SuperMeshingNet proposed in this work
can effectively boost the spatial resolution of the stress field under the
multiple scaling factors: 2X, 4X, 8X. Compared to the results of the finite
element method, the predicted stress field error of SuperMeshingNet is only
0.54%, which is within the acceptable range of stress field estimation, and the
SuperMeshingNet predicts the maximum stress value also without significant
accuracy loss. We publicly share our work with full detail of implementation at
https://github.com/zhenguonie/2021_SuperMeshing_2D_Plane_Strain.
