Plasma and Fusion Research
Volume 20, 1201059 (2025)
Rapid Communications
- College of Industrial Technology, Nihon University, Narashino 275-8575, Japan
Abstract
In this study, we apply Hankel Sparsity-Promoting Dynamic Mode Decomposition (Hankel-SP-DMD) to dynamic turbulent flows in two-dimensional space to predict their long-term spatial structures. The target turbulence data are obtained from numerical simulations based on the extended Hasegawa-Wakatani model. The proposed method successfully extracts dominant modes and predicts their temporal evolution. We further evaluate the impact of hyperparameter settings required for training on prediction performance, and provide guidance for appropriate parameter selection based on the correlation with turbulence and limit-cycle periods.
Keywords
DMD, sparse modeling, plasma turbulence, long-term prediction
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