AI 中文总结
该研究针对托卡马克H模能量约束时间预测，提出幂律锚定残差学习框架，开发PLR-KAN等模型，在插值与参数定义外推任务中实现了非线性插值能力与经验约束外推的平衡，性能优于直接KAN。
AI 中文摘要
能量约束时间的可靠预测对磁约束核聚变至关重要。传统幂律标度提供受限的外推趋势，但无法表征复杂非线性；而神经网络插值精度高，但在训练分布外可能表现不可预测。我们提出统一的幂律锚定残差学习框架，其中冻结的经验幂律标度提供全局趋势，非线性模型仅在对数空间中学习系统残差。开发PLR-KAN作为主要实现，而参数匹配的PLR-MLP作为受控架构替代方案。使用ITPA DB5.2.3 H模约束数据库，我们在10个完整训练流程上评估插值和参数定义的保留组。PLR-KAN保持接近最佳的插值精度，达到R²=0.9671±0.0027，同时在参数定义的分布偏移下显著提升直接KAN的稳定性。它在全部5个非ε单参数定义组和核心5联合组上优于直接KAN，在后者达到R²=0.9263±0.0157。PLR-MLP的结果进一步证明，幂律锚定的益处并非特定于KAN，尽管残差迁移的有效性仍依赖架构和方向。作为探索性扩展，基于马氏距离的预测时间门提升了选定偏移区域的稳定性，但并非普遍有益，且无法弥补缺失的装置或物理 regime 覆盖。总体而言，幂律锚定残差学习在非线性插值能力与经验约束外推行为间提供了实用的平衡。
英文摘要
Reliable prediction of the energy confinement time is essential for magnetic-confinement fusion. Conventional power-law scalings provide constrained extrapolation trends but cannot represent complex nonlinearities, whereas neural networks interpolate accurately but may behave unpredictably outside the training distribution. We propose a unified power-law-anchored residual-learning framework in which a frozen empirical power-law scaling supplies the global trend and a nonlinear model learns only the systematic residual in logarithmic space. PLR-KAN is developed as the primary implementation, while a parameter-matched PLR-MLP serves as a controlled architecture replacement. Using the ITPA DB5.2.3 H-mode confinement database, we evaluate interpolation and parameter-defined held-out cohorts over ten complete training pipelines. PLR-KAN retains near-best interpolation accuracy, achieving R2=0.9671+/-0.0027, while substantially improving the stability of direct KAN under parameter-defined distribution shifts. It outperforms direct KAN across all five non-epsilon single-parameter-defined cohorts and the core-five joint cohort, reaching R2=0.9263+/-0.0157 in the latter. Results from PLR-MLP further demonstrate that the benefit of power-law anchoring is not specific to KAN, although the effectiveness of residual transfer remains architecture and direction dependent. As an exploratory extension, a Mahalanobis-distance-based prediction-time gate improves stability in selected shifted regions but is not universally beneficial and cannot compensate for missing device or physics-regime coverage. Overall, power-law-anchored residual learning provides a practical balance between nonlinear interpolation capability and empirically constrained extrapolation behavior.
CommentsSubmitted to Plasma Science and Technology
