SuperMeshing: A Novel Method for Boosting the Mesh Density in Numerical Computation within 2D Domain
Handing Xu, Zhenguo Nie, Qingfeng Xu, Xinjun Liu

TL;DR
This paper introduces SuperMeshingNet, a deep neural network that significantly enhances mesh density in 2D numerical simulations, improving accuracy and efficiency simultaneously, with proven effectiveness across multiple scaling factors.
Contribution
The paper presents a novel deep learning approach using residual dense blocks to boost mesh density in 2D finite element stress analysis, achieving high accuracy with reduced computational cost.
Findings
Effective 2X, 4X, 8X mesh density enhancement
Predicted stress field error only 0.54%
Maintains maximum stress prediction accuracy
Abstract
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…
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Taxonomy
TopicsModel Reduction and Neural Networks · Computer Graphics and Visualization Techniques · Medical Imaging Techniques and Applications
