VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis

2026-05-27

AI Summary

This work addresses the complexity and expert dependency of traditional finite element analysis by proposing the first end-to-end automation framework capable of processing both image and text inputs. The approach introduces a multi-agent system grounded in ReAct-style reasoning, integrating vision-language understanding, collaborative task planning, and a verification-first code generation mechanism. To ensure physical validity, the framework incorporates self-debugging and fallback strategies. Evaluated across diverse engineering mechanics scenarios, the method substantially outperforms existing large language model baselines, demonstrating high success rates and robustness in generating complete, correct, and physically consistent simulation models.

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.

Problem

• Finite Element Analysis
• Multimodal Input
• End-to-End Automation
• Engineering Simulation
• Domain Expertise

Innovation

• Multimodal Agent
• Finite Element Analysis Automation
• Vision-Language Reasoning
• Verification-First Code Synthesis
• ReAct-based Planning