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Open Access

ARTICLE

Large Language Model-Enabled Constitutive Modeling for Rate-Dependent Plasticity and Automatic UMAT Subroutine Generation

Yuchuan Gu1,2, Lusheng Wang1,*, Jun Ding1, Yanhong Peng1, Changgeng Li3,*, Shaojie Gu4,5
1 College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China
2 Institute of Agricultural Machinery, Chongqing Academy of Agricultural Sciences, Chongqing, China
3 School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
4 Magnesium Research Center, Kumamoto University, Kumamoto, Japan
5 Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan
* Corresponding Author: Lusheng Wang. Email: email; Changgeng Li. Email: email
(This article belongs to the Special Issue: Computational Materials Design and Intelligent Processing for Advanced Alloys and Manufacturing Systems)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.075939

Received 11 November 2025; Accepted 03 February 2026; Published online 19 February 2026

Abstract

In materials science and engineering design, high-fidelity and high-efficiency numerical simulation has become a driving force for innovation and practical implementation. To address longstanding bottlenecks in the development of conventional material constitutive models—such as lengthy modeling cycles and difficulties in numerical implementation—this study proposes an intelligent modeling and code generation approach powered by large language models. A structured knowledge base integrating constitutive theory, numerical algorithms, and UMAT (User Material) interface specifications is constructed, and a retrieval-augmented generation strategy is employed to establish an end-to-end workflow spanning experimental data parsing, constitutive model formulation, and automatic UMAT subroutine generation. Experimental results show that the method achieves high accuracy for both a classical Johnson–Cook model and a physics-informed neural network (PINN) model, with key parameter identification errors below 5%. Moreover, the automatically generated UMAT subroutines yield finite element simulation results in Abaqus that are highly consistent with theoretical predictions (coefficient of determination R2 > 0.98) while maintaining good numerical stability. This framework is currently focused on the automatic construction of rate-dependent elastoplastic material models, and its core method also provides a clear path for extending to other constitutive categories such as hyperelasticity and viscoelasticity. This work provides an effective technical route for the rapid development and reliable numerical implementation of material constitutive models, significantly advancing the intelligence level of computational mechanics research and improving engineering application efficiency.

Keywords

Large language model; constitutive model; UMAT subroutine

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