Fine-Scale Feature Encoding in Geometric Pre-training for Engineering Surrogate Modeling

Abstract AI-driven surrogate modeling has emerged as an effective alternative to physics-based simulations for 3D design, analysis, and manufacturing. These models use data-driven techniques to predict physical quantities that traditionally require computationally expensive simulations. However, the scarcity of labeled CAD-to-simulation datasets has motivated the development of self-supervised and foundation models, in which geometric representation learning is performed offline and later adapted to downstream tasks using limited labeled data. While promising, existing approaches often struggle in applications that require accurate preservation of fine-scale geometric details. This work introduces a self-supervised geometric representation learning method designed to capture fine-scale geometric features from non-parametric 3D models. Unlike traditional end-to-end surrogate models, the proposed approach decouples geometric feature extraction from downstream physics prediction by learning a latent representation guided solely by geometric reconstruction losses. Key components include near-zero-level signed distance field sampling and a batch-adaptive attention-weighted loss function, which together enhance sensitivity to subtle yet physically influential geometric variations. The proposed method is validated through two case studies involving high-dimensional design parameter regression, achieving coefficients of determination exceeding 0.98, as well as structural mechanics tasks that demonstrate strong few-shot prediction performance for reaction forces and deformation fields. Comparisons with parametric surrogate models further illustrate our method's ability to bridge geometric and physics-based representations, providing an effective surrogate modeling solution in data-scarce settings.