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.
@article{arxiv.2411.06565,
title = {Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction},
author = {Ting-Ju Wei and Chuin-Shan Chen},
journal= {arXiv preprint arXiv:2411.06565},
year = {2025}
}