LatticeGraphNet: A two-scale graph neural operator for simulating lattice structures
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914948981981184 |
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| author | Jain, Ayush Haghighat, Ehsan Nelaturi, Sai |
| author_facet | Jain, Ayush Haghighat, Ehsan Nelaturi, Sai |
| contents | This study introduces a two-scale Graph Neural Operator (GNO), namely, LatticeGraphNet (LGN), designed as a surrogate model for costly nonlinear finite-element simulations of three-dimensional latticed parts and structures. LGN has two networks: LGN-i, learning the reduced dynamics of lattices, and LGN-ii, learning the mapping from the reduced representation onto the tetrahedral mesh. LGN can predict deformation for arbitrary lattices, therefore the name operator. Our approach significantly reduces inference time while maintaining high accuracy for unseen simulations, establishing the use of GNOs as efficient surrogate models for evaluating mechanical responses of lattices and structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01045 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LatticeGraphNet: A two-scale graph neural operator for simulating lattice structures Jain, Ayush Haghighat, Ehsan Nelaturi, Sai Machine Learning Computational Engineering, Finance, and Science This study introduces a two-scale Graph Neural Operator (GNO), namely, LatticeGraphNet (LGN), designed as a surrogate model for costly nonlinear finite-element simulations of three-dimensional latticed parts and structures. LGN has two networks: LGN-i, learning the reduced dynamics of lattices, and LGN-ii, learning the mapping from the reduced representation onto the tetrahedral mesh. LGN can predict deformation for arbitrary lattices, therefore the name operator. Our approach significantly reduces inference time while maintaining high accuracy for unseen simulations, establishing the use of GNOs as efficient surrogate models for evaluating mechanical responses of lattices and structures. |
| title | LatticeGraphNet: A two-scale graph neural operator for simulating lattice structures |
| topic | Machine Learning Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2402.01045 |