用于建模MAST-U台基湍流的局域线性 gyrokinetic 模拟的机器学习方法
Machine learning methods for modelling local, linear gyrokinetic simulations of MAST-U pedestal turbulence
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中文总结 AI 辅助
本研究针对MAST-U台基湍流，开发机器学习替代模型以替代高成本的GENE局域线性gyrokinetic模拟，通过特定采样工作流降低数据维度，提升了台基建模的效率。
中文摘要 AI 辅助
gyrokinetic（GK）稳定性强烈影响球形托卡马克等离子体中高约束模式台基的性能。GENE等高保真 gyrokinetic 代码可模拟微不稳定性驱动的输运，但计算成本限制了其在集成台基建模工作流中的常规使用，当前工作流常依赖简化输运假设，如EPED中使用的 ballooning-critical 台基模型。本研究针对MAST-U相关台基参数空间中的局域线性 gyrokinetic 模拟，探究机器学习替代模型，旨在为简化台基模型提供更快的基于 gyrokinetic 的输入。开发了一种采样工作流：在实验驱动的边界内改变台基剖面参数，用于生成物理自洽的Grad-Shafranov平衡，与直接采样局域gyrokinetic输入相比，该方法降低了数据生成问题的维度，同时保持了等离子体剖面、几何结构和局域稳定性参数的物理合理组合。替代模型经训练后可从局域线性GENE模拟中预测线性增长率、实频率和扩散率比输运指纹。多头多层感知机可准确复现增长率，而扩散率比和实频率表现出更聚类的、与 regime 相关的行为。采用基于频率的 regime 类的多头分类-回归模型可降低这些聚类目标的平均绝对误差，更好地捕捉与基础不稳定性 regime 变化相关的尖锐过渡，不过在模式过渡区域附近的误差仍是其局限。
英文摘要
Gyrokinetic (GK) stability strongly influences the performance of high-confinement-mode pedestals in spherical tokamak plasmas. High-fidelity gyrokinetic codes such as GENE can model microinstability-driven transport, but the computational cost limits their routine use in integrated pedestal modeling workflows. Instead, present workflows often rely on reduced transport assumptions, such as the ballooning-critical pedestal model used in EPED. This work investigates machine-learning surrogate models for local linear gyrokinetic simulations in a MAST-U-relevant pedestal parameter space, with the aim of providing faster gyrokinetic-based inputs to reduced pedestal models. A sampling workflow is developed in which pedestal profile parameters are varied within experimentally motivated bounds and used to generate physically self-consistent Grad-Shafranov equilibria. This reduces the dimensionality of the data-generation problem compared with sampling local gyrokinetic inputs directly, while maintaining physically plausible combinations of plasma profiles, geometry, and local stability parameters. The surrogate models are trained to predict linear growth rates, real frequencies, and diffusivity-ratio transport fingerprints from local linear GENE simulations. A multi-head multilayer perceptron accurately reproduces the growth rate, while the diffusivity ratios and real frequency exhibit more clustered, regime-dependent behavior. A multi-head classification-regression model using frequency-based regime classes reduces the mean absolute error for these clustered targets and better captures sharp transitions associated with changes in the underlying instability regime, although errors near mode-transition regions remain a limitation.
