Beyond empirical models: Discovering new constitutive laws in solids with graph-based equation discovery

• URL: http://arxiv.org/abs/2511.09906v1
• Date: Fri, 14 Nov 2025 01:17:44 GMT
• Title: Beyond empirical models: Discovering new constitutive laws in solids with graph-based equation discovery
• Authors: Hao Xu, Yuntian Chen, Dongxiao Zhang,
• Abstract summary: We propose a graph-based equation discovery framework for the automated discovery of laws from experimental data.<n>We have discovered new models for strain-rate effects in alloy steel materials and the deformation behavior of lithium metal.<n>The proposed framework provides a generalizable and interpretable approach for data-driven scientific modelling.
• Score: 18.41175322283189
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Constitutive models are fundamental to solid mechanics and materials science, underpinning the quantitative description and prediction of material responses under diverse loading conditions. Traditional phenomenological models, which are derived through empirical fitting, often lack generalizability and rely heavily on expert intuition and predefined functional forms. In this work, we propose a graph-based equation discovery framework for the automated discovery of constitutive laws directly from multisource experimental data. This framework expresses equations as directed graphs, where nodes represent operators and variables, edges denote computational relations, and edge features encode parametric dependencies. This enables the generation and optimization of free-form symbolic expressions with undetermined material-specific parameters. Through the proposed framework, we have discovered new constitutive models for strain-rate effects in alloy steel materials and the deformation behavior of lithium metal. Compared with conventional empirical models, these new models exhibit compact analytical structures and achieve higher accuracy. The proposed graph-based equation discovery framework provides a generalizable and interpretable approach for data-driven scientific modelling, particularly in contexts where traditional empirical formulations are inadequate for representing complex physical phenomena.

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