A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems
Summary
This paper presents AbaqusAgent, a multi-agent framework using large language models to automate finite element analysis in solid mechanics. It achieves 86% success rate on 50 problems, lowering the barrier for entry-level users and enabling human-simulation interaction.
View Cached Full Text
Cached at: 06/02/26, 03:45 PM
# A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems Source: [https://arxiv.org/abs/2606.00138](https://arxiv.org/abs/2606.00138) [View PDF](https://arxiv.org/pdf/2606.00138) > Abstract:Finite element analysis \(FEA\) is the most important numerical approach for solid mechanics\. Challenges of FEA include a steep learning curve for entry\-level users and potential false simulations due to incorrect definitions of key simulation components, such as boundary conditions, load cases, and solution variables\. Years of engineering experience are usually necessary for real\-world problem\-solving\. To address these issues, we present AbaqusAgent, a multi\-agent framework grounded in large language models \(LLMs\) for solid mechanics analyses\. AbaqusAgent is developed to facilitate analysis case generation and execution using Abaqus, one of the most widely used FEA packages, by turning users' natural\-language instructions into executed FEA analyses and result visualization\. AbaqusAgent is composed of six agents, including interpreter, architect, input writer, runner, reviewer, and visualizer agents, encompassing all the essential pre\-processing and post\-processing steps of standard FEA analyses\. A wide variety of 50 solid mechanics problems have been successfully validated, achieving an overall success rate of 86%\. Beyond improving the efficiency of FEA for solid mechanics problems and lowering the barrier to computational mechanics education, AbaqusAgent advances the human\-simulation interaction paradigm and enables integration with AI\-empowered optimization and material characterization workflows\. The code is available at[this https URL](https://github.com/LIRAM-LIN/AbaqusAgent) ## Submission history From: Shiyao Lin \[[view email](https://arxiv.org/show-email/44e20a57/2606.00138)\] **\[v1\]**Thu, 28 May 2026 23:18:33 UTC \(1,806 KB\)
Similar Articles
VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis
This paper proposes VFEAgent, a multi-agent system that automates finite element analysis by integrating vision-language models with a verification-first code synthesis framework, enabling end-to-end simulation from images and problem descriptions.
EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design
EngiAI introduces a multi-agent framework and benchmark suite for LLM-driven engineering design, evaluating workflow, RAG, and HPC dimensions. Proprietary models achieve 96-97% task completion on Beams2D, while conditional branching remains challenging with 20-53% for Photonics2D.
CAX-Agent: A Lightweight Agent Harness for Reliable APDL Automation
CAX-Agent is a lightweight agent harness for automating MAPDL finite-element simulations using large language models, with a focus on recovery policies. Evaluation shows model-based recovery achieves best completion rates.
AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents
This paper presents AI CFD Scientist, an open-source AI agent for computational fluid dynamics that autonomously discovers physics corrections using vision-language verification and code modification, outperforming general AI scientists on CFD tasks.
TradingAgents: Multi-Agents LLM Financial Trading Framework
This paper introduces TradingAgents, a multi-agent LLM framework that simulates real-world trading firms to improve stock trading performance. It utilizes specialized agents for analysis and risk management, demonstrating superior results in cumulative returns and Sharpe ratio compared to baselines.