A new combination of Hankel and sparsity-promoting dynamic mode decompositions and its application to the prediction of plasma turbulence

Akira Kusaba, Tetsuji Kuboyama, Kilho Shin, Makoto Sasaki, Shigeru Inagaki

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A new combined use of dynamic mode decomposition algorithms is proposed, which is suitable for the analysis of spatiotemporal data from experiments with few observation points, unlike computational fluid dynamics with many observation points. The method was applied to our data from a plasma turbulence experiment. As a result, we succeeded in constructing a quite accurate model for our training data and it made progress in predictive performance as well. In addition, modal patterns from the longer-term analysis help to understand the underlying mechanism more clearly, which is demonstrated in the case of plasma streamer structure. This method is expected to be a powerful tool for the data-driven construction of a reduced-order model and a predictor in plasma turbulence research and also any nonlinear dynamics researches of other applied physics fields.

Original languageEnglish
Article numberSA1011
JournalJapanese journal of applied physics
Volume61
Issue numberSA
DOIs
Publication statusPublished - Jan 2022

All Science Journal Classification (ASJC) codes

  • General Engineering
  • General Physics and Astronomy

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