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.
All Science Journal Classification (ASJC) codes
- Nuclear Energy and Engineering
- Condensed Matter Physics
Cannas, B., Fanni, A., Murari, A., Pau, A., & Sias, G. (2014). Overview of manifold learning techniques for the investigation of disruptions on JET. Plasma Physics and Controlled Fusion, 56(11), -. . https://doi.org/10.1088/0741-3335/56/11/114005