一种实现固体力学问题端到端有限元分析的多AI智能体框架

摘要

有限元分析是固体力学领域最重要的数值方法。其面临的挑战包括入门级用户陡峭的学习曲线，以及因边界条件、载荷工况和求解变量等关键仿真组件的错误定义可能导致虚假仿真结果。实际工程问题的解决通常需要多年的工程经验积累。为解决这些问题，我们提出AbaqusAgent——一个基于大语言模型的多智能体框架，专用于固体力学分析。该框架利用Abaqus（最广泛使用的有限元分析软件包之一），通过将用户自然语言指令转化为可执行的有限元分析流程及结果可视化，实现分析案例的生成与执行。AbaqusAgent由六个智能体组成，包括解释器、构建师、输入文件生成器、运行器、审查器与可视化器，覆盖标准有限元分析的所有关键前处理与后处理步骤。在50个多样化的固体力学问题验证中，该框架实现了86%的整体成功率。除提升固体力学有限元分析效率并降低计算力学的学习门槛外，AbaqusAgent还推动了人机仿真交互范式的革新，并可集成至基于人工智能的优化与材料表征工作流中。代码已开源至https://github.com/LIRAM-LIN/AbaqusAgent。

English

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