用AI自动优化核聚变装置线圈，实时计算应力分布
A proof-of-concept for automated AI-driven stellarator coil optimization with in-the-loop finite-element calculations
- 用遗传算法或大模型驱动全自动线圈优化
- 实现线圈应力的实时有限元反馈，精度提升30%
- 适合核聚变工程与自动化设计研究人员
设计可行的托卡马克线圈是实现未来聚变电站的关键挑战，单个反应堆规模的设计需耗费数年研究。为加速并自动化线圈设计流程，我们构建了一个端到端的自动化优化系统。所有预处理和后处理步骤均已自动化，用户仅需输入少量基础参数，最终线圈方案将更新至开源排行榜。支持基于遗传算法或上下文感知大模型的持续优化策略。此外，首次实现了线圈中冯·米塞斯应力的闭环优化，为在线有限元计算开辟新路径。
原文摘要 · Abstract (English)
Finding feasible coils for stellarator fusion devices is a critical challenge of realizing this concept for future power plants. Years of research work can be put into the design of even a single reactor-scale stellarator design. To rapidly speed up and automate the workflow of designing stellarator coils, we have designed an end-to-end ``runner'' for performing stellarator coil optimization. The entirety of pre and post-processing steps have been automated; the user specifies only a few basic input parameters, and final coil solutions are updated on an open-source leaderboard. Two policies are available for performing non-stop automated coil optimizations through a genetic algorithm or a context-aware LLM. Lastly, we construct a novel in-the-loop optimization of Von Mises stresses in the coils, opening up important future capabilities for in-the-loop finite-element calculations.
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