MechFoundry 论文提出无人工干预的真实物体结构分析智能体循环
MechFoundry: An agentic loop that poses and performs engineering analyses and optimization of real-world objects
AI 导读
研究者发布 MechFoundry，一个结合定制软件的智能体循环，可从物体的文字描述自动生成形状、设定载荷工况、分配材料、求解非线性结构响应并优化设计。系统基于自适应八叉树的可微分 GPU 求解器，无需网格贴合表面；在 50 个日常与工程物体上平均每物体 6.5 分钟、花费 1.3 美元完成一次工况分析，并用语言模型评审为各会话打分以指示其工程判断的强弱之处。
正文 · 原文
Abstract:AI agents can automate software engineering because a compiler or a test suite checks that the code does what the user or the agent intended. In engineering analysis, however, the steps that precede the computation have few easily verifiable checks: modeling a design or an object, deciding how it is used, where supports and loads are applied, and what material each part is made of. A human expert therefore makes these decisions, carries out the steps, and judges the result, one object at a time. We present MechFoundry, an agentic loop coupled with bespoke software that automates structural analyses of real-world objects without a human in the loop. From an object's lexical description, it generates a shape, poses a loading case relevant to how the object is used, assigns a material to each part, solves the nonlinear structural response, and improves the design against the loads it carries. A differentiable GPU solver built on an adaptive octree that need not conform to the surface enables the transition from shape generation to simulation and optimization. Across 50 everyday and engineering objects, the agent posed and solved a loading case in 6.5 minutes and for US$1.3 per object on average. Without a test suite to check against, we had language-model reviewers grade each session, as a first indication of where the agent's engineering judgment holds and where it fails.
| Subjects: | Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.14571 [cs.CE] |
| (or arXiv:2611.14571v1 [cs.CE] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14571 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xue-Ling Luo [view email]
[v1]
Mon, 9 Nov 2026 18:36:26 UTC (14,391 KB)
来源：arXiv cs.AI · arxiv.org