用大模型直接优化工程形状，省去传统算法迭代。
Using Large Language Models for Parametric Shape Optimization
- 让大模型通过自然语言提示，像进化算法一样搜索最优形状。
- 在二维机翼和三维轴对称体上均找到低阻力最优解，效果媲美基准方法。
- 比经典优化算法更快收敛，适合快速原型设计场景。
近期先进的大语言模型（LLMs）展现出上下文学习的涌现能力，可通过自然语言提示实现智能决策而无需重新训练。这一新范式在通用控制与优化问题中已显潜力。受此启发，我们探索大模型在一项关键工程任务——参数化形状优化（PSO）中的应用。提出 LLM-PSO 框架，利用大模型在进化策略精神下确定参数化工程设计的最优形状。采用 'Claude 3.5 Sonnet' 大模型，在两个基准流体优化问题上评估：1）层流中二维机翼的阻力最小化；2）斯托克斯流中三维轴对称体的阻力最小化。结果表明，LLM-PSO 在两类问题中均成功识别出与基准解一致的最优形状，且通常比经典优化算法收敛更快。初步探索为大模型在形状优化与工程设计中的更广泛应用提供了启示。
原文摘要 · Abstract (English)
Recent advanced large language models (LLMs) have showcased their emergent capability of in-context learning, facilitating intelligent decision-making through natural language prompts without retraining. This new machine learning paradigm has shown promise in various fields, including general control and optimization problems. Inspired by these advancements, we explore the potential of LLMs for a specific and essential engineering task: parametric shape optimization (PSO). We develop an optimization framework, LLM-PSO, that leverages an LLM to determine the optimal shape of parameterized engineering designs in the spirit of evolutionary strategies. Utilizing the ``Claude 3.5 Sonnet'' LLM, we evaluate LLM-PSO on two benchmark flow optimization problems, specifically aiming to identify drag-minimizing profiles for 1) a two-dimensional airfoil in laminar flow, and 2) a three-dimensional axisymmetric body in Stokes flow. In both cases, LLM-PSO successfully identifies optimal shapes in agreement with benchmark solutions. Besides, it generally converges faster than other classical optimization algorithms. Our preliminary exploration may inspire further investigations into harnessing LLMs for shape optimization and engineering design more broadly.
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