用智能代理自动优化磁约束聚变装置设计，提升等离子体稳定性和性能。
Agentic Stage-One Stellarator Optimization: Autonomous Multi-Objective Search for Finite-Beta Equilibria
- 引入智能代理自主决策优化步骤，协调多目标搜索过程。
- 在相同计算预算下，有效配置数从5个增至19个，稳定性指标提升逾半。
- 生成可复用的决策数据，适合聚变工程与人工智能交叉研究者参考。
托卡马克装置第一阶段设计需在高维三维等离子体边界空间中搜索满足约束条件的平衡解，包括约束性能、磁场拓扑、力平衡、稳定性指标和几何要求。这些条件无法直接映射出有效的有限β平衡解。高质量目标通常依赖迭代数值优化，其结果受初始构型、傅里叶分辨率、目标优先级和局部求解器预算影响。协调该过程成本高且依赖专家经验，限制了设计效率与数据一致性。本文提出一种智能体驱动的阶段一优化概念验证：基于语言模型的智能体诊断当前平衡状态并选择下一步局部优化实验，而确定性DESC执行则负责保持指定剖面、通量、对称性、度量评估、求解器有效性与接受标准。在扩展的有限β实验集的一个通用预算子集上，门有效性配置数量由5个增至19个；中位数Boozer QS RMS从2.39×10⁻⁴降至1.07×10⁻⁴，中位数最大主曲率从62.56降至33.00 m⁻¹。另一条长路径实现QS降低9.10倍，同时修复磁阱和曲率缺陷。系统还记录每次尝试的行动作为转移证据，共产生734条结构化的父-动作-结果记录。结果表明，智能体外层控制可维持有限β下的多目标搜索，并将重复优化转化为可扩展的优质平衡解与可复用决策数据源。
原文摘要 · Abstract (English)
Stage-one stellarator design searches a high-dimensional family of three-dimensional plasma boundaries and fixed-boundary MHD equilibria for configurations that jointly meet requirements on confinement, field-line topology, force balance, stability proxies, and geometry. These specifications do not provide a general constructive map to a validated finite-beta equilibrium. High-quality targets are commonly developed through iterative numerical optimization whose outcome depends on the initial configuration, active Fourier resolution, objective priorities, and local solver budget. Coordinating this process is computationally costly and expert-intensive, limiting both design throughput and the production of consistently evaluated data. We present a proof of concept for \emph{agentic} stage-one optimization. A bounded language-model agent diagnoses the current equilibrium and selects the next local optimization experiment, while deterministic DESC execution owns prescribed profiles and flux, symmetry, metric evaluation, solver validity, and acceptance. On a common-budget subset from an expanding finite-beta campaign, the number of gate-valid configurations increases from five inputs to nineteen outputs; median Boozer QS RMS decreases from $2.39\times10^{-4}$ to $1.07\times10^{-4}$, and median maximum principal curvature decreases from $62.56$ to $33.00\,\mathrm{m}^{-1}$. A complementary long route achieves a $9.10\times$ QS reduction while repairing magnetic-well and curvature defects. The system also records every attempted local action as transition evidence, yielding 734 structured parent--action--outcome records in the reported experiments. These results show that agentic outer-loop control can sustain finite-beta, multi-objective search and turn repeated optimization into a scalable source of improved equilibria and reusable decision data.
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