Overview of manifold learning techniques for the investigation of disruptions on JET

B. Cannas, A. Fanni, A. Murari, A. Pau, G. Sias

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Identifying a low-dimensional embedding of a high-dimensional data set allows exploration of the data structure. In this paper we tested some existing manifold learning techniques for discovering such embedding within the multidimensional operational space of a nuclear fusion tokamak. Among the manifold learning methods, the following approaches have been investigated: linear methods, such as principal component analysis and grand tour, and nonlinear methods, such as self-organizing map and its probabilistic variant, generative topographic mapping. In particular, the last two methods allow us to obtain a low-dimensional (typically two-dimensional) map of the high-dimensional operational space of the tokamak.
Original languageEnglish
Article number114005
Pages (from-to)-
JournalPlasma Physics and Controlled Fusion
Volume56
Issue number11
DOIs
Publication statusPublished - 1 Nov 2014
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Nuclear Energy and Engineering
  • Condensed Matter Physics

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