Jason Chadwick

Quantum computing Ph.D. student
University of Chicago
jchadwick@uchicago.edu

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Figure 1. Relative explained variance of principal components for the NSTX-U training data. We use principal component analysis (PCA) to reduce the dimensionality of the density and pressure profiles. In this work, modes with an explained variance ratio greater than $10^{-3}$ are kept. This leads to keeping 7 modes for the pressure profile shape and 9 for the density profile shape.