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

--> Computer Science > Artificial Intelligence arXiv:2606.00138 (cs) [Submitted on 28 May 2026] Title: A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems Authors: Titu Ranjan Sarker , Muhammed Jawaad Zulqernine , Ling Yue , Shaowu Pan , Chenxi Wang , Shiyao Lin View a PDF of the paper titled A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems, by Titu Ranjan Sarker and 5 other authors View PDF 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 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.00138 [cs.AI] (or arXiv:2606.00138v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.00138 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Shiyao Lin [ view email ] [v1] Thu, 28 May 2026 23:18:33 UTC (1,806 KB) Full-text links: Access Paper: View a PDF of the paper titled A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems, by Titu Ranjan Sarker and 5 other authors View PDF TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )