多智能体系统自动完成从图像到工程报告的全流程分析。
From Perception to Autonomous Computational Modeling: A Multi-Agent Approach
- 用受控迭代的智能体协同处理从感知数据到报告生成的全流程。
- 对钢L型支架分析生成17万节点网格，发现结构失效并给出重设计建议。
- 适合需要自动化工程评估但需工程师最终审核的场景。
我们提出一种与求解器无关的框架，由协调的大语言模型（LLM）智能体自主执行完整的计算力学流程：从工程构件的感知数据出发，经几何提取、材料推断、离散化、求解器运行、不确定性量化到符合规范的评估，最终生成包含可操作建议的工程报告。智能体被形式化为共享上下文空间上的条件算子，通过质量门控实现各流程层间的条件迭代。我们引入数学框架，利用区间边界、概率密度和模糊隶属函数从不确定感知数据中提取工程信息，并根据任务需求设定保守性，以解决不同极限状态参数趋势相反时‘保守’定义模糊的问题。该框架在有限元分析流程中演示，对一张钢L型支架照片进行分析，生成171,504个节点的四面体网格，开展三种边界条件假设下的七次分析，最终完成合规性评估，揭示结构失效并量化提出重设计方案。所有结果均在首次自主迭代中生成，无需人工修正，强调专业工程师仍须审查并签字确认任何此类分析。
原文摘要 · Abstract (English)
We present a solver-agnostic framework in which coordinated large language model (LLM) agents autonomously execute the complete computational mechanics workflow, from perceptual data of an engineering component through geometry extraction, material inference, discretisation, solver execution, uncertainty quantification, and code-compliant assessment, to an engineering report with actionable recommendations. Agents are formalised as conditioned operators on a shared context space with quality gates that introduce conditional iteration between pipeline layers. We introduce a mathematical framework for extracting engineering information from perceptual data under uncertainty using interval bounds, probability densities, and fuzzy membership functions, and introduce task-dependent conservatism to resolve the ambiguity of what `conservative' means when different limit states are governed by opposing parameter trends. The framework is demonstrated through a finite element analysis pipeline applied to a photograph of a steel L-bracket, producing a 171,504-node tetrahedral mesh, seven analyses across three boundary condition hypotheses, and a code-compliant assessment revealing structural failure with a quantified redesign. All results are presented as generated in the first autonomous iteration without manual correction, reinforcing that a professional engineer must review and sign off on any such analysis.
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