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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.
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.
StampFormer is a physics-guided deep learning framework that fuses geometry and material properties to predict FEA outcomes for sheet metal stamping in under a second, achieving high fidelity with less than 8.5% relative error.
This paper introduces a new task formulation for CAD generation that incorporates finite element analysis as feedback, along with improved supervision signals like a text-only blueprint schema and multi-view image renderer, leading to better geometric reconstruction on benchmarks.