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Computer Science > Machine Learning

arXiv:2504.08621 (cs)
[Submitted on 11 Apr 2025]

Title:MooseAgent: A LLM Based Multi-agent Framework for Automating
Moose Simulation

Authors:Tao Zhang, Zhenhai Liu, Yong Xin, Yongjun Jiao
View a PDF of the paper titled MooseAgent: A LLM Based Multi-agent
Framework for Automating Moose Simulation, by Tao Zhang and 3 other
authors
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    Abstract: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 this https URL

Comments: 7 pages, 2 Figs
Subjects: Machine Learning (cs.LG); Software Engineering (cs.SE)
Cite as:  arXiv:2504.08621 [cs.LG]
          (or arXiv:2504.08621v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2504.08621
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Tao Zhang [view email]
[v1] Fri, 11 Apr 2025 15:25:50 UTC (134 KB)
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