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

Researchers have developed AbaqusAgent, a multi-agent AI framework that automates finite element analysis (FEA) for solid mechanics problems by converting natural language instructions into executable simulations. The system achieved an 86% success rate across 50 validated problems and aims to democratize FEA by reducing the technical barrier to entry for non-expert users.

AbaqusAgent represents a significant advancement in making computational mechanics more accessible through AI automation. The framework addresses a fundamental challenge in engineering: FEA requires specialized expertise, extensive training, and careful configuration of complex parameters. By leveraging large language models to interpret natural language instructions and coordinate multiple specialized agents (interpreter, architect, input writer, runner, reviewer, visualizer), the system eliminates tedious pre-processing and post-processing workflows that typically consume significant engineering time.

The 86% success rate across diverse solid mechanics problems demonstrates practical viability for real-world applications. This capability particularly impacts engineering education and prototyping workflows, where students and practitioners can now iterate on designs without deep FEA expertise. The reduction in barriers to computational mechanics democratizes a previously specialized field, potentially accelerating innovation in product design and structural analysis.

The broader implications extend beyond FEA automation. AbaqusAgent's architecture enables seamless integration with AI-driven optimization and material characterization workflows, suggesting a future where design iteration becomes predominantly AI-assisted. This shift could fundamentally alter how engineers approach problem-solving, moving from manual configuration toward collaborative human-AI workflows. The open-source release enhances adoption potential across academic and industrial settings.

Future development should focus on expanding the success rate toward 95%+ for production environments and testing edge cases in complex multi-physics simulations. Integration with other FEA packages beyond Abaqus would broaden applicability. Monitoring how enterprises adopt this technology reveals whether AI-assisted engineering becomes standard practice across the industry.

• →AbaqusAgent converts natural language instructions into executed FEA simulations with 86% success rate, significantly lowering technical barriers.
• →The multi-agent framework automates both pre-processing and post-processing steps typically requiring years of engineering experience.
• →System architecture enables future integration with AI optimization and material characterization workflows, advancing human-simulation interaction.
• →Open-source availability accelerates adoption across academic institutions and engineering organizations globally.
• →Success demonstrates viability of LLM-based automation for complex domain-specific technical tasks beyond software development.