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

TL;DR

This paper introduces MMAE, a self-supervised Vision Transformer for microstructure analysis, enabling accurate stiffness prediction and nonlinear behavior inference from limited data, advancing composite material modeling.

Contribution

The paper proposes MMAE, a novel foundation model for microstructures, capable of transfer learning for stiffness and nonlinear response prediction, with potential for extension to complex systems.

Findings

MMAE effectively predicts homogenized stiffness with limited data.

Coupling MMAE with IMN enables extrapolation of stress-strain responses.

The model captures essential microstructural features transferable across tasks.

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.