Finn H O’Shea, Semin Joung, David R Smith, Daniel Ratner, Ryan Coffee
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引用次数: 0
摘要
使用监督学习来训练机器学习模型,以预测即将发生的边缘局部模式(ELM),需要大量的标记样本。从 DIII-D 等长期运行的托卡马克放电的庞大数据库中创建一个适当的数据集,对人类来说是一个非常耗时的过程。考虑到这一需求和困难,我们使用了巧合异常检测(一种无监督学习技术)来训练 ELM 识别器,以识别和标记 DIII-D 放电数据库中的 ELM。该 ELM 识别器同时显示,在从跨越五年的数千个出院数据中提取的示例时间序列中识别 ELM 的精确度为 0.68,召回率为 0.63(AUC 为 0.73)。在一个包含 50 个出院数据的测试集中,该算法发现了超过 2.6 万个 ELM 候选,是现有人工标注 ELM 目录的 5 倍多。
Coincidence anomaly detection for unsupervised locating of edge localized modes in the DIII-D tokamak dataset
Using supervised learning to train a machine learning model to predict an on-coming edge localized mode (ELM) requires a large number of labeled samples. Creating an appropriate data set from the very large database of discharges at a long-running tokamak, such as DIII-D, would be a very time-consuming process for a human. Considering this need and difficulty, we use coincidence anomaly detection, an unsupervised learning technique, to train an ELM-identifier to identify and label ELMs in the DIII-D discharge database. This ELM-identifier shows, simultaneously, a precision of 0.68 and a recall of 0.63 (AUC is 0.73) on identifying ELMs in example time series pulled from thousands of discharges spanning five years. In a test set of 50 discharges, the algorithm finds over 26 thousand ELM candidates, more than 5 times the existing catalog of ELMs labeled by humans.
期刊介绍:
Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.