用大模型的上下文学习能力加速可靠度设计优化，提升效率与可行性。
Generative Reliability-Based Design Optimization Using In-Context Learning Capabilities of Large Language Models
- 结合大模型上下文学习与元启发式算法迭代搜索
- 在三次案例中实现可靠度约束下的性能优化，收敛速度相当传统遗传算法
- 适合需要快速生成高可靠性设计方案的工程场景
大型语言模型（LLMs）展现出强大的上下文学习能力，能够灵活利用有限的历史信息，在推理、问题求解和复杂模式识别任务中发挥关键作用。受此启发，本文提出一种基于大模型上下文学习能力的生成式可靠度设计优化方法，结合元启发式算法的迭代搜索机制，解决可靠度设计优化问题。具体地，通过调用大模型并结合Kriging代理模型进行可靠性分析，以克服计算负担。通过提示工程动态向大模型提供设计点的关键信息，实现满足可靠性约束且性能最优的高质量设计方案的快速生成。采用Deepseek-V3模型进行三项案例研究，实验结果表明，所提的LLM-RBDO方法成功找到满足可靠性约束的可行解，并在收敛速度上与传统遗传算法相当。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated remarkable in-context learning capabilities, enabling flexible utilization of limited historical information to play pivotal roles in reasoning, problem-solving, and complex pattern recognition tasks. Inspired by the successful applications of LLMs in multiple domains, this paper proposes a generative design method by leveraging the in-context learning capabilities of LLMs with the iterative search mechanisms of metaheuristic algorithms for solving reliability-based design optimization problems. In detail, reliability analysis is performed by engaging the LLMs and Kriging surrogate modeling to overcome the computational burden. By dynamically providing critical information of design points to the LLMs with prompt engineering, the method enables rapid generation of high-quality design alternatives that satisfy reliability constraints while achieving performance optimization. With the Deepseek-V3 model, three case studies are used to demonstrated the performance of the proposed approach. Experimental results indicate that the proposed LLM-RBDO method successfully identifies feasible solutions that meet reliability constraints while achieving a comparable convergence rate compared to traditional genetic algorithms.
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