Building models that generalize across physical systems without retraining remains a central challenge in computational science. Here we introduce In-Context Modeling (ICM), a retrain-free paradigm that infers physical relationships directly from observational fields. Rather than encoding system-specific behavior in fixed parameters, ICM assimilates measurements as physical context and performs inference through a single forward pass. Trained in a physics-informed, label-free manner using governing equations, a single model generalizes across unseen materials, geometries, and loading conditions. Demonstrated on hyperelasticity, ICM integrates with finite-element simulations and is validated using experimental full-field measurements. Moreover, performance improves with increasing data diversity and computational budget, exhibiting favorable scaling behavior analogous to foundation models. By recasting physical modeling as in-context inference, this work establishes a transferable paradigm for retrain-free scientific learning and a foundation for scalable modeling across computational science.

翻译：在计算科学中，构建无需重训练即可跨物理系统泛化的模型仍是一项核心挑战。本文提出上下文建模（ICM）——一种直接从观测场推断物理关系的无重训练范式。ICM不将系统特定行为编码为固定参数，而是将测量值吸收为物理上下文，并通过单次前向传播完成推理。基于控制方程以物理驱动、无标签方式训练的单一模型可跨未见材料、几何构型与载荷条件进行泛化。经超弹性案例验证，ICM与有限元仿真集成，并通过实验全场测量数据获得验证。此外，其性能随数据多样性与计算预算的增加而提升，展现出类似基础模型的有利缩放行为。通过将物理建模重塑为上下文推断，本研究建立了可迁移的无重训练科学学习范式，并为计算科学中的可扩展建模奠定了基础。