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-consum...

תיאור מלא

מידע ביבליוגרפי

Principais autores:	Finn H O’Shea, Semin Joung, David R Smith, Daniel Ratner, Ryan Coffee
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	IOP Publishing 2024-01-01
סדרה:	Machine Learning: Science and Technology
נושאים:	machine learning unsupervised learning tokamak plasma fusion
גישה מקוונת:	https://doi.org/10.1088/2632-2153/ad6be7
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!