用主动学习和不确定性神经网络，高效构建等离子体湍流输运模型的小规模数据集。
Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models
- 结合主动学习与不确定感知模型，利用物理仿真自动标注数据。
- 仅用100到10000条数据，分类和回归性能分别达F1≈0.8、R²≈0.75。
- 方法可推广至其他复杂系统建模，尤其适合高成本仿真场景。
本研究证明了将不确定性感知架构与主动学习技术相结合，并通过闭环物理仿真代码作为数据标注器，可高效构建用于数据驱动代理模型生成的小规模数据集。基于此前在静态预标注数据集上成功实现训练集缩减的ADEPT框架，该策略被再次应用于托卡马克聚变等离子体中的湍流输运问题，具体针对QuaLiKiz准线性静电吉罗动力学湍流输运代码。尽管QuaLiKiz计算较快，本研究仍聚焦于小样本数据集，以模拟更昂贵的CGYRO或GENE代码。新算法采用SNGP处理分类任务，BNN-NCP处理回归任务，覆盖所有湍流模式（ITG、TEM、ETG）及所有输运通量（Q_e、Q_i、Γ_e、Γ_i、Π_i）。经过45轮主动学习，初始训练集从10²增长至10⁴，最终模型在独立测试集上达到约F1=0.8的分类性能和约R²=0.75的回归性能，整体表现与先前ADEPT管道相当，但输入维度多一个。虽改进速率低于预期，但整体框架具备可升级性和通用性，适用于多种代理建模任务。
原文摘要 · Abstract (English)
This work demonstrates a proof-of-principle for using uncertainty-aware architectures, in combination with active learning techniques and an in-the-loop physics simulation code as a data labeller, to construct efficient datasets for data-driven surrogate model generation. Building off of a previous proof-of-principle successfully demonstrating training set reduction on static pre-labelled datasets, using the ADEPT framework, this strategy was applied again to the plasma turbulent transport problem within tokamak fusion plasmas, specifically the QuaLiKiz quasilinear electrostatic gyrokinetic turbulent transport code. While QuaLiKiz provides relatively fast evaluations, this study specifically targeted small datasets to serve as a proxy for more expensive codes, such as CGYRO or GENE. The newly implemented algorithm uses the SNGP architecture for the classification component of the problem and the BNN-NCP architecture for the regression component, training models for all turbulent modes (ITG, TEM, ETG) and all transport fluxes ($Q_e$, $Q_i$, $Γ_e$, $Γ_i$, and $Π_i$) described by the general QuaLiKiz output. With 45 active learning iterations, moving from a small initial training set of $10^{2}$ to a final set of $10^{4}$, the resulting models reached a $F_1$ classification performance of ~0.8 and a $R^2$ regression performance of ~0.75 on an independent test set across all outputs. This extrapolates to reaching the same performance and efficiency as the previous ADEPT pipeline, although on a problem with 1 extra input dimension. While the improvement rate achieved in this implementation diminishes faster than expected, the overall technique is formulated with components that can be upgraded and generalized to many surrogate modeling applications beyond plasma turbulent transport predictions.
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