Graph-based Methods for Orbit Classification

Conference · Thu Sep 29 00:00:00 EDT 2005

DOI:https://doi.org/10.1137/1.9781611972764.63· OSTI ID:885368

Bagherjeiran, A; Kamath, C

An important step in the quest for low-cost fusion power is the ability to perform and analyze experiments in prototype fusion reactors. One of the tasks in the analysis of experimental data is the classification of orbits in Poincare plots. These plots are generated by the particles in a fusion reactor as they move within the toroidal device. In this paper, we describe the use of graph-based methods to extract features from orbits. These features are then used to classify the orbits into several categories. Our results show that existing machine learning algorithms are successful in classifying orbits with few points, a situation which can arise in data from experiments.

• Research Organization: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: W-7405-ENG-48
• OSTI ID: 885368
• Report Number(s): UCRL-CONF-215802; TRN: US0603873
• Resource Relation: Conference: Presented at: SIAM International Conference on Data Mining, Bethesda, MD, United States, Apr 20 - Apr 22, 2006
• Country of Publication: United States
• Language: English

Related Subjects

70 PLASMA PHYSICS AND FUSION TECHNOLOGY
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ALGORITHMS
CLASSIFICATION
LEARNING
MINING
THERMONUCLEAR REACTORS