一個多AI智能體框架實現固體力學問題的端到端有限元素分析

摘要

有限元分析（FEA）是固体力学中最重要的数值方法。其应用面临的挑战包括入门门槛高、以及因边界条件、载荷工况和求解变量等关键模拟参数设置错误可能导致的仿真结果失真。解决实际工程问题通常需要数年的工程经验。针对这些难题，我们提出基于大语言模型（LLMs）的多智能体框架AbaqusAgent，专用于固体力学分析。通过将用户自然语言指令转化为可执行的有限元分析与结果可视化，该框架实现了借助Abaqus（应用最广泛的有限元分析软件之一）的分析案例生成与执行自动化。AbaqusAgent由六大智能体模块构成：解释器、架构师、输入文件生成器、运行器、审查器和可视化器，完整覆盖标准有限元分析的前处理与后处理全流程。在涵盖50个不同固体力学问题的验证测试中，整体成功率达86%。该框架不仅提升了固体力学有限元分析的效率并降低了计算力学的教育门槛，更推动了人机仿真交互范式革新，为AI驱动的优化设计与材料表征工作流程提供了集成接口。源代码已开源发布于 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