Physics-Audited Agentic Discovery in Scientific Machine Learning
In agentic scientific machine learning (SciML), large language model (LLM) agents can discover surrogate models and select one by an automated score, typically an error metric. A low error, however, does not establish that the predicted fields satisfy the physics that matter for mechanics, such as boundary conditions, superposition, stiffness scaling, or causality. We introduce Physics-Audited Agentic SciML (PA-SciML), a verification-first workflow for agentic SciML discovery. The workflow fixes a scoring evaluator before search, derives reviewable machine-checkable physics requirements, checks each trained candidate on its outputs, and separately searches prescribed input ranges or measu...
| Authors | Diab W. Abueidda, Bilal Ahmed, Panos Pantidis et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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