VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis
VFEAgent is an end-to-end multi-agent system that automates finite element analysis (FEA) modeling and simulation directly from input images and problem descriptions. It combines a multimodal vision-language multi-agent pipeline with a verification-first code synthesis framework, using ReAct-driven reasoning to extract structured FEA specifications and incorporating self-debugging and fallback mechanisms for executability and physical validity. Experiments show high success rates in generating complete, physically valid simulations, outperforming LLM-based baselines in reliability and correctness, and promising to free engineers from tedious manual analysis.
Article intelligence
Key points
- VFEAgent automates FEA modeling and simulation from images and problem descriptions.
- Employs a multimodal vision-language multi-agent pipeline with ReAct-driven reasoning.
- Features a verification-first code synthesis framework with self-debugging and fallback mechanisms.
- Outperforms LLM-based methods in reliability and correctness across engineering mechanics scenarios.
Why it matters
This matters because vFEAgent automates FEA modeling and simulation from images and problem descriptions.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.28978] VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis
[Submitted on 27 May 2026]
Title:VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis
View a PDF of the paper titled VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis, by Jiachen Zhang (1 and 2) and 8 other authors
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Abstract:Finite Element Analysis (FEA) serves as the cornerstone of modern engineering design. However, its workflow is inherently complex and relies heavily on domain expertise. Although recent efforts have integrated Large Language Models (LLMs) into FEA, existing approaches face limitations in handling multimodal inputs and executing complex tasks. To address these limitations, we propose VFEAgent, an end-to-end multi-agent system designed to automate FEA modeling and simulation directly from input images and problem descriptions. Our methodology integrates two core components: (1) a multimodal vision-language multi-agent pipeline that employs ReAct-driven reasoning to extract structured FEA specifications from heterogeneous inputs and (2) a verification-first code synthesis framework, incorporating robust self-debugging and fallback mechanisms to ensure executability and physical validity. We systematically evaluated the system across various engineering mechanics scenarios. The results demonstrate that VFEAgent achieves a high success rate in generating complete and physically valid simulations, outperforming LLM-based baseline methods in reliability and correctness. These findings validate the feasibility of automating the complete FEA workflow, highlighting the framework's potential to liberate engineers from tedious manual analysis.
Comments: 9 pages, 3 figures, 2 tables. Equal contribution: Jiachen Zhang and Junyi Lao. Corresponding author: Songfang Huang. Preprint
Subjects:
Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
Cite as: arXiv:2605.28978 [cs.AI]
(or arXiv:2605.28978v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.28978
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jiachen Zhang [view email] [v1] Wed, 27 May 2026 18:34:04 UTC (11,350 KB)
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