A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems

2026-05-28

AI Summary

This work proposes AbaqusAgent, the first end-to-end, natural language–driven multi-agent framework for finite element analysis (FEA) in solid mechanics, designed to lower the barrier to entry and reduce reliance on expert knowledge. The framework integrates six collaborative modules—Interpreter, Architect, Input Generator, Runner, Reviewer, and Visualizer—to automatically translate user-provided natural language instructions into a complete Abaqus workflow encompassing pre-processing, solving, and post-processing. Evaluated on 50 solid mechanics problems, the system achieves an 86% success rate, significantly enhancing simulation efficiency. Furthermore, it advances human–computer interaction paradigms and enables seamless integration with AI-driven optimization and material characterization pipelines.

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 https://github.com/LIRAM-LIN/AbaqusAgent

Problem

• Finite Element Analysis
• Solid Mechanics
• Simulation Errors
• User Accessibility
• Computational Mechanics

Innovation

• multi-agent framework
• large language models
• finite element analysis
• natural language interface
• solid mechanics