LLM-driven design of physics-constrained constitutive models: two agents are better than one
Read the original on arXiv Machine Learning →The paper presents a novel multi‑agent approach to generating physics‑constrained constitutive models using large language models (LLMs). A Creator agent proposes a model tailored to the data, while an Inspector agent audits each proposal against nine physical constraints and requests refinement if violations occur. Experiments with constitutive artificial neural networks on brain tissue, rubber, and porcine skin show that adding the Inspector increases the proportion of models passing all checks from 90 % to 95 % for Claude Opus 4.7 and from 47 % to 60 % for Kimi K2.5, while the resulting models match or exceed expert‑designed models in accuracy and generalization.
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