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In stress field analysis, the finite element analysis is a crucial approach,in which the mesh-density has a significant impact on the results. High meshdensity usually contributes authentic to simulation results but costs morecomputing resources, leading to curtailing efficiency during the designprocess. To eliminate this drawback, we propose a new data-driven mesh-densityboost model named SuperMeshingNet that strengthens the advantages of finiteelement analysis (FEA) with low mesh-density as inputs to the deep learningmodel, which consisting of Res-UNet architecture, to acquire high-densitystress field instantaneously, shortening computing time and cost automatically.Moreover, the attention mechanism and the perceptual features are utilized,enhancing the performance of SuperMeshingNet. Compared to the baseline thatapplied the linear interpolation method, SuperMeshingNet achieves a prominentreduction in the mean squared error (MSE) and mean absolute error (MAE) on testdata, which contains prior unseen cases. Based on the data set of metalforming, the comparable experiments are proceeded to demonstrate the highquality and superior precision of the reconstructed results generated by ourmodel. The well-trained model can successfully show more excellent performancethan the baseline and other methods on the multiple scaled mesh-density,including $2\times$, $4\times$, and $8\times$. With the refined result owningbroaden scaling of mesh density and high precision, the FEA process can beaccelerated with seldom cost on computation resources. We publicly share ourwork with full detail of implementation athttps://github.com/zhenguonie/2021_SuperMeshing_2D_Metal_Forming

SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh  Density of Metal Forming Stress Field with Attention Mechanism and Perceptual  Features