Geometry-Aware OOD Generalization for Composite Materials

Abstract

Machine learning methods have recently shown promise in predicting composite material properties more efficiently than traditional finite element simulations. However, in real-world applications of composite materials, variations in filler characteristics or processing conditions can cause a significant mismatch between the training and test distributions, leading to degradation in model performance. Existing approaches to domain adaptation typically aim to learn feature representations that are invariant across distribution shifts or minimize worst case risk across predefined environments, which are not suitable for composite materials where the distribution shifts are feature driven and localized. In this work, we argue that such shifts are more naturally characterized by the geometry of the data manifold rather than Euclidean distances in the ambient feature space. We reinterpret OOD generalization under covariate shift as a problem of preserving geodesic relationships on the data manifold. Building on this perspective, we propose Geometry Support Anchoring (GSA), a geometry-aware learning framework that anchors predictions using geodesic distances to data-dependent reference representations. For our approach, we have developed a multimodal conditional Joint Embedding Predictive Architecture (JEPA) that learns invariant, material-relevant representations by predicting latent targets using context from complementary modalities. By enforcing consistency in intrinsic geometry rather than raw features, our approach preserves physically meaningful variation while improving robustness to regime shifts. Experiments on both public and in-house composite material datasets with realistic distribution shifts demonstrate that our method consistently improves robustness and OOD generalization compared to state-of-the-art tabular learning and domain adaptation methods.