用大模型迭代提示优化核反应堆设计，效果优于传统方法。
Optimization through In-Context Learning and Iterative LLM Prompting for Nuclear Engineering Design Problems
- 用自然语言描述问题，通过大模型不断试错优化
- 在沸水堆燃料格栅设计中，性能超越传统优化算法
- 无需调参，适合非专业人员快速尝试复杂设计
核工程设计（如核燃料组件布局）需平衡反应性控制与功率分布等多重目标。本文探索基于提示的优化方法，利用大语言模型（LLMs）进行迭代优化，仅需描述问题和编写评估脚本，无需超参数调优或复杂数学建模。得益于大模型的上下文学习能力，其能理解问题细节，在沸水堆（BWR）燃料格栅设计任务中表现出优于传统元启发式优化方法的性能。实验表明，商用大模型可有效充当优化器，实现高质量设计求解。
原文摘要 · Abstract (English)
The optimization of nuclear engineering designs, such as nuclear fuel assembly configurations, involves managing competing objectives like reactivity control and power distribution. This study explores the use of Optimization by Prompting, an iterative approach utilizing large language models (LLMs), to address these challenges. The method is straightforward to implement, requiring no hyperparameter tuning or complex mathematical formulations. Optimization problems can be described in plain English, with only an evaluator and a parsing script needed for execution. The in-context learning capabilities of LLMs enable them to understand problem nuances, therefore, they have the potential to surpass traditional metaheuristic optimization methods. This study demonstrates the application of LLMs as optimizers to Boiling Water Reactor (BWR) fuel lattice design, showing the capability of commercial LLMs to achieve superior optimization results compared to traditional methods.
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