Zhang, Zheyan;
Jimack, Peter K;
Wang, He;
(2021)
MeshingNet3D: Efficient generation of adapted tetrahedral meshes for computational mechanics.
Advances in Engineering Software
, 157
, Article 103021. 10.1016/j.advengsoft.2021.103021.
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Abstract
We describe a new algorithm for the generation of high quality tetrahedral meshes using artificial neural networks. The goal is to generate close-to-optimal meshes in the sense that the error in the computed finite element (FE) solution (for a target system of partial differential equations (PDEs)) is as small as it could be for a prescribed number of nodes or elements in the mesh. In this paper we illustrate and investigate our proposed approach by considering the equations of linear elasticity, solved on a variety of three-dimensional geometries. This class of PDE is selected due to its equivalence to an energy minimization problem, which therefore allows a quantitative measure of the relative accuracy of different meshes (by comparing the energy associated with the respective FE solutions on these meshes). Once the algorithm has been introduced it is evaluated on a variety of test problems, each with its own distinctive features and geometric constraints, in order to demonstrate its effectiveness and computational efficiency.
| Type: | Article |
|---|---|
| Title: | MeshingNet3D: Efficient generation of adapted tetrahedral meshes for computational mechanics |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.advengsoft.2021.103021 |
| Publisher version: | https://doi.org/10.1016/j.advengsoft.2021.103021 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | ALGORITHM, Artificial neural networks, Computer Science, Computer Science, Interdisciplinary Applications, Computer Science, Software Engineering, Engineering, Engineering, Multidisciplinary, Finite element methods, Machine learning, NEURAL-NETWORKS, Optimal mesh generation, Science & Technology, Technology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215238 |
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