Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction
This work addresses the challenge of efficient material property prediction for composite materials, offering a novel foundation model approach that is incremental in applying existing self-supervised methods to a new domain.
The paper tackles the problem of predicting material properties from composite microstructure images by introducing the Material Masked Autoencoder (MMAE), a self-supervised Vision Transformer pretrained on a large dataset, which achieves transferable latent representations for tasks like predicting stiffness components and inferring nonlinear stress-strain responses.
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.