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首页> 外文期刊>Plasma physics and controlled fusion >An alternative approach to the determination of scaling law expressions for the L–H transition in Tokamaks utilizing classification tools instead of regression
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An alternative approach to the determination of scaling law expressions for the L–H transition in Tokamaks utilizing classification tools instead of regression

机译：使用分类工具而不是回归方法来确定托卡马克州L–H跃迁的比例定律表达式的另一种方法

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摘要

A new approach to determine the power law expressions for the threshold between the H and L mode of confinement is presented. The method is based on two powerful machine learning tools for classification: neural networks and support vector machines. Using as inputs clear examples of the systems on either side of the transition, the machine learning tools learn the input–output mapping corresponding to the equations of the boundary separating the confinement regimes. Systematic tests with synthetic data show that the machine learning tools provide results competitive with traditional statistical regression and more robust against random noise and systematic errors. The developed tools have then been applied to the multi-machine International Tokamak Physics Activity International Global Threshold Database of validated ITER-like Tokamak discharges. The machine learning tools converge on the same scaling law parameters obtained with non-linear regression. On the other hand, the developed tools allow a reduction of 50% of the uncertainty in the extrapolations to ITER. Therefore the proposed approach can effectively complement traditional regression since its application poses much less stringent requirements on the experimental data, to be used to determine the scaling laws, because they do not require examples exactly at the moment of the transition.
机译：提出了一种确定H和L限制模式之间的阈值的幂律表达式的新方法。该方法基于用于分类的两个强大的机器学习工具：神经网络和支持向量机。机器学习工具使用过渡两边的系统的清晰示例作为输入，来学习输入-输出映射，该输入-输出映射对应于将约束制度分开的边界方程。使用合成数据进行的系统测试表明，机器学习工具提供的结果与传统的统计回归相比具有竞争优势，并且在抵抗随机噪声和系统错误方面更强大。然后将已开发的工具应用于经多机验证的类似ITER的托卡马克排放物的多机国际托卡马克物理活动国际全球阈值数据库。机器学习工具收敛于通过非线性回归获得的相同比例定律参数。另一方面，开发的工具可以将ITER推断的不确定性降低50％。因此，所提出的方法可以有效地补充传统回归，因为它的应用对实验数据的严格要求要低得多，可用于确定比例定律，因为它们在过渡时不需要确切的示例。

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