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This paper introduces AbaqusAgent, a multi-agent framework leveraging large language models (LLMs) to streamline finite element analysis (FEA) for solid mechanics problems. By transforming natural language instructions into executable FEA analyses, the framework significantly reduces the steep learning curve associated with traditional FEA methods, achieving an impressive 86% success rate across 50 validated problems. The approach not only enhances efficiency and accessibility in computational mechanics but also sets the stage for improved human-simulation interactions and integration with AI-driven optimization workflows.
AbaqusAgent turns natural language into successful finite element analyses, achieving an 86% success rate and transforming how engineers interact with simulation tools.
Finite element analysis (FEA) is the most important numerical approach for solid mechanics. Challenges of FEA include a steep learning curve for entry-level users and potential false simulations due to incorrect definitions of key simulation components, such as boundary conditions, load cases, and solution variables. Years of engineering experience are usually necessary for real-world problem-solving. To address these issues, we present AbaqusAgent, a multi-agent framework grounded in large language models (LLMs) for solid mechanics analyses. AbaqusAgent is developed to facilitate analysis case generation and execution using Abaqus, one of the most widely used FEA packages, by turning users'natural-language instructions into executed FEA analyses and result visualization. AbaqusAgent is composed of six agents, including interpreter, architect, input writer, runner, reviewer, and visualizer agents, encompassing all the essential pre-processing and post-processing steps of standard FEA analyses. A wide variety of 50 solid mechanics problems have been successfully validated, achieving an overall success rate of 86%. Beyond improving the efficiency of FEA for solid mechanics problems and lowering the barrier to computational mechanics education, AbaqusAgent advances the human-simulation interaction paradigm and enables integration with AI-empowered optimization and material characterization workflows. The code is available at https://github.com/LIRAM-LIN/AbaqusAgent
