Researchers at the Eastern Institute of Technology (EIT), Ningbo, have developed a graph-based approach that directly extracts concise, accurate constitutive equations from solid-material experimental data. The study, published in Science Advances, describes a method for discovering constitutive models for alloy steels, lithium metal and filled rubbers. It outperforms mainstream empirical models in predictive accuracy while preserving explicit, physically interpretable mathematical formulations.

This breakthrough addresses a longstanding limitation in solid mechanics: the conventional reliance on predefined empirical formulas to characterize the complex mechanical responses of metallic and nonmetallic materials. “Constitutive models are foundational to solid mechanics. Traditionally, researchers derive mathematical forms based on physical intuition and subsequently calibrate model parameters using experimental data,” said Hao Xu, EIT postdoctoral researcher and lead author of the study.

“Although this paradigm has achieved great success in mechanics research, predetermined equation structures inherently restrict the model’s descriptive and predictive capability. Our framework shifts the research paradigm: it starts purely from experimental data and employs artificial intelligence to autonomously search for and identify optimal constitutive equations.”