Constitutive scientific generative agent (CSGA): Leveraging large language models for automated constitutive model discovery

Abstract Data-driven approaches for constitutive modeling enable rapid, automated generation of models that predict a material’s mechanical response under load. Integrating theoretical knowledge into these approaches, which are then called grey-box approaches, can improve sample efficiency, extrapolation capability, and interpretability, albeit typically at the cost of experts required to use them. Recently, general-purpose large language model (LLM)-based scientific discovery methods have emerged as user-friendly approaches to scientific discovery. In this work, we compare two representatives of these paradigms: highly specialized constitutive artificial neural networks (CANNs) and the general LLM-based scientific generative agent (SGA) to evaluate current LLM capabilities in constitutive modeling. In addition, we introduce the constitutive scientific generative agent (CSGA) to combine both approaches’ strengths by enriching the SGA’s prompts with domain-specific data and materials theory. We compare CANN, SGA, and CSGA on three benchmark problems by assessing their accuracy in predicting stress responses under prescribed strain conditions. While our results show that CANNs remain the most accurate approach overall, the CSGA significantly outperforms the SGA and demonstrates the promise of specialized LLM-based methods for constitutive modeling. Moreover, the CSGA’s intuitive plain text interface and the full interpretability of the generated constitutive models make it a practical, accessible complement to existing approaches.