Topology optimization is a widely used design method that produces optimized material distributions for prescribed objectives and constraints through well-established numerical algorithms. Throughout the workflow, engineers make a series of decisions ranging from setting and adjusting numerical parameters to assessing whether the converged design meets considerations beyond those explicitly included in the optimization problem, such as physical feasibility. These decisions, which draw on domain expertise, interfere with the autonomous design process. To address this difficulty, this study presents TopOptAgents, a multi-agent system for automating not only the design process but also decision-making during the key stages of the topology optimization process. TopOptAgents consists of six LLM-based agents collaborating through iterative self-refinement cycles spanning problem formulation, validation, code generation and execution, and quality assessment of the optimized structure. This process enables error correction and progressive improvement of both the optimization setup and resulting design. The framework is demonstrated on optimization problems selected to cover a range of settings that differ in their literature coverage and numerical characteristics The benefits of iterative self-refinement are found to be particularly pronounced for problem classes where the pretrained language model has limited prior exposure, such as formulations whose literature and open-source implementations are comparatively sparse. In such cases, the proposed framework reliably produces converged designs where a single state-of-the-art LLM struggles, suggesting that self-refinement broadens the range of topology optimization problems that LLM-based automation can reliably address.

翻译：拓扑优化是一种广泛使用的设计方法，通过成熟的数值算法，在给定目标和约束下生成最优材料分布。在整个工作流程中，工程师需要做出从设定和调整数值参数，到评估收敛设计是否满足优化问题中未明确包含的考虑因素（如物理可行性）等一系列决策。这些依赖领域专业知识的决策，阻碍了全自动设计流程的实现。为解决这一难题，本研究提出TopOptAgents——一个不仅自动化设计流程，还能在拓扑优化的关键阶段实现决策自动化的多智能体系统。TopOptAgents由六个基于大语言模型的智能体组成，它们通过迭代自优化循环协同工作，涵盖问题建模、验证、代码生成与执行、以及优化结构的质量评估。该流程使得优化设置和最终设计能够实现错误修正和渐进式改进。该框架在覆盖不同文献丰富度和数值特性的优化问题上进行了验证。研究发现，对于预训练语言模型先前暴露有限的问题类别（例如文献和开源实现相对较少的公式），迭代自优化的优势尤为显著。在此类情况下，所提框架能可靠地生成收敛的设计，而单个最先进的大语言模型则难以实现，这表明自优化机制拓宽了基于大语言模型的自动化方法能够可靠处理的拓扑优化问题范围。