Zhang, Z, Jimack, PK orcid.org/0000-0001-9463-7595 and Wang, H orcid.org/0000-0002-2281-5679 (2021) MeshingNet3D: Efficient generation of adapted tetrahedral meshes for computational mechanics. Advances in Engineering Software, 157-158. 103021. ISSN 0965-9978
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. This is an author produced version of an article published in Advances in Engineering Software. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 May 2021 08:30 |
Last Modified: | 01 Mar 2023 12:58 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.advengsoft.2021.103021 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173988 |