用于昂贵的仿真驱动设计的搜索硬度感知基于大语言模型的问题公式化
Search Hardness-Aware LLM-Based Problem Formulation for Expensive Simulation-Driven Design
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中文总结 AI 辅助
针对昂贵仿真驱动设计中公式对搜索效率的影响问题，提出SHA-PF框架，基于搜索硬度定义公式搜索目标并评分，通过大语言模型相关方法搜索公式空间，实验表明该框架发现的公式能显著减少评估次数以达设计要求。
中文摘要 AI 辅助
昂贵的仿真驱动设计在工程中广泛应用，旨在用尽可能少的高保真仿真确定满足需求的设计。多数现有工作通过改进固定公式下的优化算法应对挑战，但公式本身会塑造搜索格局。基于大语言模型的自动问题公式化方法主要关注设计意图对齐，忽视公式是否能引导高效搜索。为此提出SHA-PF框架，发现优先考虑有更大进展潜力的稀有样本的公式更易引导高效搜索，据此定义由搜索硬度引导的公式搜索目标并评分，通过基于大语言模型的生成、修复和进化优化搜索公式空间。在实际多目标基准和五个昂贵天线设计基准上的实验表明，SHA-PF发现的公式比其他基线所需评估次数显著更少。
英文摘要
Expensive simulation-driven design is widely used in engineering to identify requirement-satisfying designs with as few high-fidelity simulations as possible. Most existing efforts address this challenge by improving optimization algorithms under fixed formulations, yet the formulation itself shapes the search landscape by defining the objectives and constraints optimized by the solver. Recent LLM-based automatic problem formulation methods generate formulations from natural-language requirements, but they mainly focus on design-intent alignment and overlook whether the formulation induces an efficient search process. To address this limitation, we propose SHA-PF, a search hardness-aware LLM-based problem formulation framework. We find that a formulation is more likely to guide efficient search when it prioritizes rare samples with greater progress potential. Based on this finding, SHA-PF defines a formulation search objective guided by search hardness, scoring each candidate formulation according to the priority. SHA-PF then searches the formulation space under this objective through LLM-based generation, repair, and evolutionary refinement. Experiments on the real-world multi-objective benchmark and five expensive antenna design benchmarks show that the formulations discovered by SHA-PF require significantly fewer evaluations to reach the design requirements than other baselines.
