In-context modeling as a retrain-free paradigm for foundation models in computational science
For computational scientists, ICM provides a transferable paradigm that eliminates retraining for new physical systems, addressing a central challenge in the field.
ICM enables retrain-free generalization across physical systems by inferring relationships from observational fields, achieving performance that scales with data diversity and computational budget, validated on hyperelasticity with experimental data.
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.