论文标题
使用不同的机器学习技术的Tokamak中断预测
Tokamak disruption prediction using different machine learning techniques
论文作者
论文摘要
破坏预测和缓解在可持续tokamakractors的发展中至关重要。机器学习已成为这项工作的关键工具。在本文中，将测试和比较多个机械学习模型。这是对其可移植性的特殊重点。这描述了如何轻松地将模型与新设备的数据一起使用。该论文中使用的方法是支持向量机，2层支撑向量机，随机森林，梯度Boostedtrees和长期术语记忆。结果表明，在标准模型中，支持向量机的性能略有更好，而梯度增强的树的表现最差。每个模型的便携式变体的性能较低。随机森林获得了最高的便携式性能。结果还表明，在事件发生前可以在600毫秒内检测到中断。对计算成本的分析显示，所有模型的运行均小于1ms，从而使减轻破坏的时间足够。
Disruption prediction and mitigation is of key importance in the development of sustainable tokamakreactors. Machine learning has become a key tool in this endeavour. In this paper multiple machinelearning models will be tested and compared. A particular focus has been placed on their portability.This describes how easily the models can be used with data from new devices. The methods used inthis paper are support vector machine, 2-tiered support vector machine, random forest, gradient boostedtrees and long-short term memory. The results show that the support vector machine performanceis marginally better among the standard models, while the gradient boosted trees performed the worst.The portable variant of each model had lower performance. Random forest obtained the highest portableperformance. Results also suggest that disruptions can be detected as early as 600ms before the event.An analysis of the computational cost showed all models run in less than 1ms, allowing sufficient timefor disruption mitigation.
