用大模型+多智能体自动构建有限元模拟输入文件，降低使用门槛。
MooseAgent: A LLM Based Multi-agent Framework for Automating Moose Simulation
- 通过自然语言理解用户需求，分解任务并迭代验证生成配置文件。
- 在热传导、力学等场景中实现自动化，简单问题成功率高。
- 结合向量数据库减少幻觉，适合工程仿真初学者和自动化需求者。
有限元方法（FEM）广泛应用于工程与科学计算，但其预处理、求解器配置和后处理阶段耗时且需专业知识。本文提出针对多物理场仿真框架MOOSE的自动化解决方案MooseAgent，该框架将大规模预训练语言模型（LLMs）与多智能体系统结合，利用LLMs理解用户以自然语言描述的仿真需求，并通过任务分解与多轮迭代验证策略自动生成MOOSE输入文件。为提升准确性并减少模型幻觉，系统构建并使用包含标注的MOOSE输入卡与函数文档的向量数据库。在热传导、力学、相场及多物理场耦合等典型案例上进行实验评估，结果表明，MooseAgent可在一定程度上实现MOOSE仿真的自动化，尤其在相对简单的单物理场问题上表现优异。本研究的主要贡献是提出一个面向MOOSE的多智能体自动化框架，验证了其在简化有限元仿真流程、降低用户门槛方面的潜力，为智能有限元仿真软件的发展提供新思路。本文提出的MooseAgent框架代码已开源，地址为https://github.com/taozhan18/MooseAgent。
原文摘要 · Abstract (English)
The Finite Element Method (FEM) is widely used in engineering and scientific computing, but its pre-processing, solver configuration, and post-processing stages are often time-consuming and require specialized knowledge. This paper proposes an automated solution framework, MooseAgent, for the multi-physics simulation framework MOOSE, which combines large-scale pre-trained language models (LLMs) with a multi-agent system. The framework uses LLMs to understand user-described simulation requirements in natural language and employs task decomposition and multi-round iterative verification strategies to automatically generate MOOSE input files. To improve accuracy and reduce model hallucinations, the system builds and utilizes a vector database containing annotated MOOSE input cards and function documentation. We conducted experimental evaluations on several typical cases, including heat transfer, mechanics, phase field, and multi-physics coupling. The results show that MooseAgent can automate the MOOSE simulation process to a certain extent, especially demonstrating a high success rate when dealing with relatively simple single-physics problems. The main contribution of this research is the proposal of a multi-agent automated framework for MOOSE, which validates its potential in simplifying finite element simulation processes and lowering the user barrier, providing new ideas for the development of intelligent finite element simulation software. The code for the MooseAgent framework proposed in this paper has been open-sourced and is available at https://github.com/taozhan18/MooseAgent
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