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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2411.06565 |
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| _version_ | 1866914106822361088 |
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| author | Wei, Ting-Ju Chen, Chuin-Shan |
| author_facet | Wei, Ting-Ju Chen, Chuin-Shan |
| contents | We present the Material Masked Autoencoder (MMAE), a self-supervised Vision Transformer pretrained on a large corpus of short-fiber composite images via masked image reconstruction. The pretrained MMAE learns latent representations that capture essential microstructural features and are broadly transferable across tasks. We demonstrate two key applications: (i) predicting homogenized stiffness components through fine-tuning on limited data, and (ii) inferring physically interpretable parameters by coupling MMAE with an interaction-based material network (IMN), thereby enabling extrapolation of nonlinear stress-strain responses. These results highlight the promise of microstructure foundation models and lay the groundwork for future extensions to more complex systems, such as 3D composites and experimental datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06565 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction Wei, Ting-Ju Chen, Chuin-Shan Computational Engineering, Finance, and Science Artificial Intelligence We present the Material Masked Autoencoder (MMAE), a self-supervised Vision Transformer pretrained on a large corpus of short-fiber composite images via masked image reconstruction. The pretrained MMAE learns latent representations that capture essential microstructural features and are broadly transferable across tasks. We demonstrate two key applications: (i) predicting homogenized stiffness components through fine-tuning on limited data, and (ii) inferring physically interpretable parameters by coupling MMAE with an interaction-based material network (IMN), thereby enabling extrapolation of nonlinear stress-strain responses. These results highlight the promise of microstructure foundation models and lay the groundwork for future extensions to more complex systems, such as 3D composites and experimental datasets. |
| title | Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence |
| url | https://arxiv.org/abs/2411.06565 |