聚变平衡挑战：无磁诊断下的磁几何推断
The Fusion Equilibrium Challenge: Inferring Magnetic Geometry Without Magnetic Diagnostics
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- Sophelio
- General Atomics（通用原子能）
- University of Texas, Austin（德克萨斯大学奥斯汀分校）
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中文总结 AI 辅助
该研究提出聚变平衡挑战，提供多机器聚变基准数据集，要求仅用非磁诊断推断等离子体平衡，设两项奖项评估模型的机内重建与跨机泛化能力，为反应堆就绪型平衡推断提供基准。
中文摘要 AI 辅助
SPARC、ARC和CFETR等下一代聚变反应堆装置将在极端中子环境中运行，这种环境会损害传统用于重建等离子体平衡的磁传感器。然而，等离子体平衡的可靠知识——包括磁通量面、安全因子剖面和成形参数——对于实时控制、避免破裂和物理解释是必不可少的。聚变平衡挑战邀请NeurIPS社区应对一个看似简单但科学严谨的逆问题：仅通过非磁诊断，即外部极向场线圈电流和汤姆逊散射电子温度/密度剖面，来重建二维极向通量函数ψ(R,Z)和一组标量平衡参数。该挑战提供了首个开放获取、统一的多机器聚变基准，发布了经过筛选的9113次DIII-D放电和2416次MAST放电的数据集，筛选标准为汤姆逊诊断可用性、特征完整性和EFIT重建质量。每次放电被打包为标准Parquet文件，包含约260个（DIII-D）/约80个（MAST）EFIT通量图和丰富的高采样率诊断数据。两个互补奖项分别奖励DIII-D的机内重建保真度（S_model）和拓扑不同的MAST球形托卡马克的零样本跨机泛化能力（G_ratio）。我们认为，该挑战可作为反应堆就绪型平衡推断的基准，也可用于探究机器学习能在多大程度上推进到真正的机器不可知等离子体状态估计。
英文摘要
Next-generation fusion reactor devices such as SPARC, ARC, and CFETR will operate in extreme neutron environments that compromise the magnetic sensors traditionally used to reconstruct plasma equilibria. However, reliable knowledge of the plasma equilibrium--including magnetic flux surfaces, safety factor profiles, and shaping parameters--is indispensable for real-time control, disruption avoidance, and physics interpretation. The Fusion Equilibrium Challenge invites the NeurIPS community to confront a deceptively simple but scientifically rigorous inverse problem: reconstruct the two-dimensional poloidal flux function psi(R,Z) and a suite of scalar equilibrium parameters from non-magnetic diagnostics alone, namely external poloidal-field coil currents and Thomson-scattering electron temperature/density profiles. The challenge provides the first open-access, harmonized multi-machine benchmark for fusion, releasing a curated dataset of 9,113 DIII-D shots and 2,416 MAST shots--filtered for Thomson-diagnostic availability, feature completeness, and EFIT-reconstruction quality. Each shot is packaged into a standard Parquet file containing approximately 260 (DIII-D) / approximately 80 (MAST) EFIT flux maps and rich high-rate diagnostics. Two complementary awards reward intra-machine reconstruction fidelity (S_model) on DIII-D and zero-shot cross-machine generalization (G_ratio) to the topologically distinct MAST spherical tokamak. We argue that the challenge functions as a benchmark for reactor-ready equilibrium inference and as a probe of how far machine learning can be pushed toward truly machine-agnostic plasma state estimation.
