VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis
Quick Answer
VFEAgent is a novel end-to-end multi-agent framework that automates Finite Element Analysis (FEA) from images and problem descriptions.
Quick Take
It integrates a vision-language pipeline and a verification-first code synthesis framework, achieving a high success rate in generating valid simulations, outperforming existing -based methods in reliability and correctness.
Key Points
- VFEAgent automates FEA modeling and simulation from multimodal inputs.
- Employs ReAct-driven reasoning for structured FEA specifications extraction.
- Features robust self-debugging and fallback mechanisms for code synthesis.
- Demonstrated high success rates in various engineering mechanics scenarios.
- Outperformed LLM-based methods in reliability and correctness.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2605. 28978v1 Announce Type: new 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 (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 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.
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