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Asahi, Yuichi; Fujii, Keisuke*; Maeyama, Shinya*; Idomura, Yasuhiro
no journal, ,
We propose to use a dimensionality reduction technique, namely principal component analysis (PCA) to extract patterns from the series of 5D gyrokinetic plasma simulation data. It is shown that 83% of the variance of the original 6D (5D phase space + 1D time) data can be expressed with 64 principal components. Through the detailed analysis of the contribution of each principal component to the energy flux, we demonstrate that the avalanche like energy transport is driven by coherent mode structures in the phase space, indicating the key role of resonant particles.
Oba, Masaki
no journal, ,
Dimension reduction was performed using PCA on 462 types of training data obtained by processing Gd
O
, TiO, ZrOdata. After learning, the content rates of elements were analyzed using 62 types of data from actual samples as test data. Similar to last time, create a calibration curve of the true value and analytical value of the actual sample and analyze the content ratio. The neural network used this time had a configuration of input layer, middle layer (2 layers), and output layer, and the middle layer used 2 layers with 100 nodes each. As a result of PCA on the training data, we were able to significantly reduce the 7944 dimensions (pixels) of the training data to 5 dimensions. As a result of training using this and analyzing test data, the difference from the true value was approximately 10%, which was almost the same as the previous value.