Conventional mechanical design paradigms rely on experts systematically refining concepts through experience-guided modification and FEA to meet specific requirements. However, this approach can be time-consuming and heavily dependent on prior knowledge and experience. While numerous machine learning models have been developed to streamline this intensive and expert-driven iterative process, these methods typically demand extensive training data and considerable computational resources. Furthermore, methods based on deep learning are usually restricted to the specific domains and tasks for which they were trained, limiting their applicability across different tasks. This creates a trade-off between the efficiency of automation and the demand for resources. In this study, we present a novel approach that integrates pre-trained LLMs with a FEM module. The FEM module evaluates each design and provides essential feedback, guiding the LLMs to continuously learn, plan, generate, and optimize designs without the need for domain-specific training. We demonstrate the effectiveness of our proposed framework in managing the iterative optimization of truss structures, showcasing its capability to reason about and refine designs according to structured feedback and criteria. Our results reveal that these LLM-based agents can successfully generate truss designs that comply with natural language specifications with a success rate of up to 90%, which varies according to the applied constraints. By employing prompt-based optimization techniques we show that LLM based agents exhibit optimization behavior when provided with solution-score pairs to iteratively refine designs to meet specifications. This ability of LLM agents to produce viable designs and optimize them based on their inherent reasoning capabilities highlights their potential to develop and implement effective design strategies autonomously.

翻译：传统机械设计范式依赖专家通过经验引导的修改和有限元分析，系统性地优化概念以满足特定需求。然而，这种方法往往耗时且高度依赖先验知识和经验。尽管已有众多机器学习模型被开发用于简化这一高强度的专家驱动迭代过程，但这些方法通常需要大量训练数据和可观的计算资源。此外，基于深度学习的方法通常局限于其训练时所用的特定领域和任务，限制了其在不同任务间的适用性。这导致了自动化效率与资源需求之间的权衡。在本研究中，我们提出了一种创新方法，将预训练的大语言模型与有限元分析模块相结合。有限元分析模块评估每个设计并提供关键反馈，引导大语言模型持续学习、规划、生成和优化设计，而无需领域特定训练。我们展示了所提框架在管理桁架结构迭代优化中的有效性，展现了其根据结构化反馈和标准进行推理并完善设计的能力。研究结果表明，这些基于大语言模型的智能体能够成功生成符合自然语言规范的桁架设计，成功率高达90%，该成功率随施加的约束条件而变化。通过采用基于提示的优化技术，我们证实当提供解-分数对以迭代优化设计满足规范时，基于大语言模型的智能体展现出优化行为。大语言模型智能体凭借其固有推理能力生成可行设计并进行优化的能力，凸显了其自主开发并实施有效设计策略的潜力。