用自监督学习构建复合材料微结构基础模型，可预测刚度与非线性响应。
Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction
- 基于掩码图像重建训练视觉变压器，学习微结构通用表征。
- 仅需少量数据即可准确预测均质刚度分量，且能外推非线性应力-应变关系。
- 适合材料设计、仿真加速领域研究者使用，尤其关注多尺度建模者。
我们提出材料掩码自编码器（MMAE），一种在短纤维复合材料图像大规模语料上通过掩码图像重建进行自监督预训练的视觉变换器。预训练后的MMAE学习到捕捉关键微结构特征的潜在表示，具有广泛的跨任务可迁移性。我们展示了两个核心应用：(i) 通过在有限数据上微调，预测均质刚度分量；(ii) 将MMAE与基于相互作用的材料网络（IMN）结合，推断物理可解释参数，从而实现非线性应力-应变响应的外推。这些结果凸显了微结构基础模型的潜力，并为未来扩展至更复杂系统（如3D复合材料和实验数据集）奠定基础。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。