Plasma and Fusion Research

Volume 14, 1301157 (2019)

Letters

Reconstruction of Time Series Observed in Linear Magnetized Plasma PANTA via a Machine Learning Algorithm
Yasuhiro NARIYUKI, Makoto SASAKI1), Tohru HADA2) and Shigeru INAGAKI1)
Faculty of Human Development, University of Toyama, 3190 Gofuku, Toyama City, Toyama 930-8555, Japan
1)
Research Institute for Applied Mechanics, Kyushu University, 6-1 Kasugakoen, Kasuga City, Fukuoka 816-8580, Japan
2)
Faculty of Engineering Sciences, Kyushu University, 6-1 Kasugakoen, Kasuga City, Fukuoka 816-8580, Japan
(Received 24 April 2019 / Accepted 2 August 2019 / Published 9 October 2019)

Abstract

Reconstruction of turbulence time series in a statistically stationary state is discussed by using a machine learning algorithm. We use data obtained by Langmuir probes in the Plasma Assembly for Nonlinear Turbulence Analysis (PANTA). It is shown that even if the distance between two probes is not adequate to resolve the turbulence, the nonlinear regression via the machine learning can give reconstruction better than those by the linear regression and the linear interpolation. Wave forms and frequency spectra show that drift waves are well reconstructed by the machine learning.

Keywords

drift wave, linear magnetized plasma, machine learning, turbulence, coarse-grained model

DOI: 10.1585/pfr.14.1301157

Full Text

PDF format (564Kbytes) PDF Download

References

  • [1] P.H. Diamond, S.-I. Itoh, K. Itoh and T.S. Hahm, Plasma Phys. Control. Fusion 47, R35 (2005).
  • [2] T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Second edition), Springer Series in Statistics, (Springer-Verlag, New-York, 2009).
  • [3] K. Florios, I. Kontogiannis, S.-H. Park, J.A. Guerra, F. Benvenuto, D.S. Bloomfield and M.K. Georgoulis, Solar Phys. 293, 28 (2018).
  • [4] N. Nishizuka, K. Sugiura, Y. Kubo, M. Den, S. Watari and M. Ishii, Astrophys. J. 835, 156 (2017).
  • [5] S. Inagaki, T. Kobayashi, Y. Kosuga, S.-I. Itoh, T. Mitsuzono, Y. Nagashima, H. Arakawa, T. Yamada, Y. Miwa, N. Kasuya, M. Sasaki, M. Lesur, A. Fujisawa and K. Itoh, Scientific Rep. 6, 22189 (2016).
  • [6] L. Breiman, Machine Learning 45, 5 (2001).
  • [7] T. Kobayashi, S. Inagaki, M. Sasaki, Y. Kosuga, H. Arakawa, F. Kin, T. Yamada, Y. Nagashima, N. Kasuya, A. Fujisawa, S.-I. Itoh and K. Itoh, Plasma Fusion Res. 12, 1401019 (2017).
  • [8] R. Core Team, see http://www.R-project.org/ for R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2018).
  • [9] A. Liaw and M. Wiener, R News 2(3), 18 (2002).
  • [10] C. Liu, N. Deng, J.T.L. Wang and H. Wang, Astrophys. J. 843, 104 (2017).
  • [11] P.R. Vlachas, W. Byeon, Z.Y. Wan, T.P. Sapsis and P. Koumoutsakos, Proc. R. Soc. A 474, 20170844 (2017).
  • [12] M.A. Gruet, M. Chandorkar, A. Sicard and E. Camporeale, Space Weather 16, 1882 (2018).
  • [13] D.R. Ferreira and JET Contributors, arXiv:1811.00333v1 [physics.plasm-ph] (2018).
  • [14] J.-X. Wang, J.-L. Wu and H. Xiao, Phys. Rev. Fluids 2, 034603 (2017).
  • [15] J.-L. Wu, J.-X. Wang, H. Xiao and J. Ling, Flow Turbulence Combust 99(1), 25 (2017).