# In-context modeling as a retrain-free paradigm for foundation models in computational science

arXiv: https://arxiv.org/abs/2604.23098 · HTML: https://mlsanity.com/paper/2604.23098/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2026.

## Built on by (most cited first, 1 of 1)

- [Chain of Operators: An Inference-Time Harness for In-Context Operator Learning](https://mlsanity.com/paper/2606.12318/) (2026)

## Abstract (arXiv)

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.