Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction

• URL: http://arxiv.org/abs/2411.06565v4
• Date: Mon, 14 Jul 2025 07:43:50 GMT
• Title: Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction
• Authors: Ting-Ju Wei, Chuin-Shan Chen,
• Abstract summary: We present the Material Masked Autoencoder (MMAE), a self-supervised Vision Transformer pretrained on a large corpus of short-fiber composite images.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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