让AI设计零件时实时验证物理规律，提升工程可靠性。
Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design
- 将工程工具嵌入AI决策循环，实现物理验证闭环
- 生成设计复杂度提升4.2，编译成功率提高3.5%
- 适合需要高可靠性设计的工业AI研发人员
大型语言模型可生成计算机辅助设计（CAD），但缺乏可靠的物理理解。我们提出一种混合智能体-物理架构，将经验证的知识型工程工具直接嵌入自主AI智能体的决策循环中。工程设计被建模为由显式物理验证引导的闭合回路、序列化决策过程。基于载荷工况，专用智能体迭代地规划、生成、评估并修正设计方案，使用知识型工具作为反馈信号。我们构建了一个基准数据集和评估指标，用于衡量生成式CAD的功能有效性。相比同类智能体方法，本系统生成的设计更复杂且通过物理验证，结构复杂度提升4.2，编译成功率提高3.5%。代码、提示和数据集将公开，以支持可复现性和未来研究。
原文摘要 · Abstract (English)
Large Language Models (LLMs) can generate Computer-Aided Design (CAD), yet lack physical comprehension required for reliable engineering design. Instead of attempting to implicitly learn physical laws from data, we propose a Hybrid Agentic-Physical Architecture that embeds validated knowledge-based engineering tools directly into the decision making loop of autonomous AI agents. In this framework, engineering design is formulated as a closed-loop, sequential decision making process guided by explicit physical verification. Based on a load case, dedicated agents iteratively plan, generate, evaluate, and revise engineering designs using knowledge-based tools as a feedback signal. We introduce a benchmark dataset and metrics for assessing functional validity in generative CAD. Our system generates more complex and physically verified designs, with a 4.2 increase in structural complexity and improving compile rate by 3.5% compared to similar agentic methods. The codebase, prompts and dataset will be made publicly available to support reproducibility and future research.
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