Physics-Audited Agentic Discovery in Scientific Machine Learning

• URL: http://arxiv.org/abs/2607.07379v1
• Date: Wed, 08 Jul 2026 13:10:35 GMT
• Title: Physics-Audited Agentic Discovery in Scientific Machine Learning
• Abstract summary: Physics-Audited Agentic SciML (PA-SciML) is a verification-first workflow for agentic SciML discovery.<n>PA-SciML fixes a scoring evaluator before search, derives reviewable machine-checkable physics requirements, and searches for prescribed input ranges or measured load-history spans.<n>When enabled, the workflow also adds advisory numerical probes before training and tests one modeling change at a time to record which isolated edits are associated with score gains before reuse.
• Score: 1.4632791454491378
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 measured load-history spans for high-violation cases without reference solution fields. A surrogate is reported as verified only under the stated checks. When enabled, the workflow also adds advisory numerical probes before training and tests one modeling change at a time to record which isolated edits are associated with score gains before reuse. In the reported computational-solid-mechanics numerical examples, the static elasticity run selects a surrogate with lower validation error than the error-only baseline while both selected models pass the common linear-elastic checks. In the transient elastodynamics run, an error-only baseline with similar mean error fails a stricter causality check by responding to future parts of the loading history, while the selected surrogate passes the stated checks. The main distinction is per-candidate physics evidence on predicted fields, not a richer aggregate score.

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