Developing a Silicon Beam Emulator Using Graph Attention Networks
This study pioneers the application of Graph Attention Networks (GAT) and Graph Neural Networks (GNN), to the emulation of MEMS silicon beams under external loadings, representing a significant advancement in MEMS simulation. The novel augmented graph generation technique employed in this research e...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10766502/ |
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| _version_ | 1841542590780932096 |
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| author | Siyuan Lv Qianxi Cheng Haojie Gong Hao Gao Dong Zhou Zheng Duanmu |
| author_facet | Siyuan Lv Qianxi Cheng Haojie Gong Hao Gao Dong Zhou Zheng Duanmu |
| author_sort | Siyuan Lv |
| collection | DOAJ |
| description | This study pioneers the application of Graph Attention Networks (GAT) and Graph Neural Networks (GNN), to the emulation of MEMS silicon beams under external loadings, representing a significant advancement in MEMS simulation. The novel augmented graph generation technique employed in this research enhances model performance and computational efficiency compared to traditional GNN message passing, particularly in data augmentation for training robust GAT. Silicon beam simulations were conducted under varying pressure conditions, and the results were found to be consistent with the theoretical solution, which can be a complementary roles of structural analysis combined with traditional finite element analysis (FEA). The trained GAT achieved a remarkable accuracy, with over 91% of predictions exhibiting an error no more than 3%, while the computation time for a single graph remained under 2 milliseconds. In the testing phase, the GAT-based computational simulation demonstrated a significant speed advantage, over 20 times faster than conventional FEA. The successful application of GAT in this context promises to reshape multiple aspects of technology development and optimization, paving the way for more efficient and accurate simulations across a wide range of disciplines. |
| format | Article |
| id | doaj-art-e401c92d9d1d4c82b3c84e30c2a61a74 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e401c92d9d1d4c82b3c84e30c2a61a742025-01-14T00:01:28ZengIEEEIEEE Access2169-35362024-01-011217911917912910.1109/ACCESS.2024.350560310766502Developing a Silicon Beam Emulator Using Graph Attention NetworksSiyuan Lv0https://orcid.org/0009-0009-6034-1740Qianxi Cheng1Haojie Gong2Hao Gao3https://orcid.org/0000-0001-6852-9435Dong Zhou4Zheng Duanmu5https://orcid.org/0000-0002-3594-7179School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, ChinaSchool of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, ChinaSchool of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, ChinaSchool of Mathematics and Statistics, University of Glasgow, Glasgow, U.K.Chinese Academy of Sciences, Institute of Mechanics, Beijing, ChinaSchool of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, ChinaThis study pioneers the application of Graph Attention Networks (GAT) and Graph Neural Networks (GNN), to the emulation of MEMS silicon beams under external loadings, representing a significant advancement in MEMS simulation. The novel augmented graph generation technique employed in this research enhances model performance and computational efficiency compared to traditional GNN message passing, particularly in data augmentation for training robust GAT. Silicon beam simulations were conducted under varying pressure conditions, and the results were found to be consistent with the theoretical solution, which can be a complementary roles of structural analysis combined with traditional finite element analysis (FEA). The trained GAT achieved a remarkable accuracy, with over 91% of predictions exhibiting an error no more than 3%, while the computation time for a single graph remained under 2 milliseconds. In the testing phase, the GAT-based computational simulation demonstrated a significant speed advantage, over 20 times faster than conventional FEA. The successful application of GAT in this context promises to reshape multiple aspects of technology development and optimization, paving the way for more efficient and accurate simulations across a wide range of disciplines.https://ieeexplore.ieee.org/document/10766502/Graph neural networksgraph attention networksMEMS silicon beamsfinite element analysisaugmented graph generation |
| spellingShingle | Siyuan Lv Qianxi Cheng Haojie Gong Hao Gao Dong Zhou Zheng Duanmu Developing a Silicon Beam Emulator Using Graph Attention Networks IEEE Access Graph neural networks graph attention networks MEMS silicon beams finite element analysis augmented graph generation |
| title | Developing a Silicon Beam Emulator Using Graph Attention Networks |
| title_full | Developing a Silicon Beam Emulator Using Graph Attention Networks |
| title_fullStr | Developing a Silicon Beam Emulator Using Graph Attention Networks |
| title_full_unstemmed | Developing a Silicon Beam Emulator Using Graph Attention Networks |
| title_short | Developing a Silicon Beam Emulator Using Graph Attention Networks |
| title_sort | developing a silicon beam emulator using graph attention networks |
| topic | Graph neural networks graph attention networks MEMS silicon beams finite element analysis augmented graph generation |
| url | https://ieeexplore.ieee.org/document/10766502/ |
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