Zhang, Z, Wang, Y orcid.org/0000-0002-5673-042X, Jimack, PK et al. (1 more author) (2020) MeshingNet: A New Mesh Generation Method based on Deep Learning. In: Lecture Notes in Computer Science. ICCS 2020: International Conference on Computational Science, 03-05 Jun 2020, Amsterdam, The Netherlands. Springer Verlag , pp. 186-198. ISBN 978-3-030-50419-9
Abstract
We introduce a novel approach to automatic unstructured mesh generation using machine learning to predict an optimal finite element mesh for a previously unseen problem. The framework that we have developed is based around training an artificial neural network (ANN) to guide standard mesh generation software, based upon a prediction of the required local mesh density throughout the domain. We describe the training regime that is proposed, based upon the use of a posteriori error estimation, and discuss the topologies of the ANNs that we have considered. We then illustrate performance using two standard test problems, a single elliptic partial differential equation (PDE) and a system of PDEs associated with linear elasticity. We demonstrate the effective generation of high quality meshes for arbitrary polygonal geometries and a range of material parameters, using a variety of user-selected error norms.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2020. This is an author produced version of a conference paper published inLecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Mesh generation; Error equidistribution; Machine learning; Artificial neural networks |
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: | 16 Apr 2020 13:47 |
Last Modified: | 04 May 2021 15:10 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-3-030-50420-5_14 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:159526 |