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Dynamic mode decomposition for Koopman spectral analysis of elementary cellular automata
Keisuke Taga1, Yuzuru Kato2, Yoshihiro Yamazaki1
1Department of Physics, School of Advanced Science and Engineering, Waseda University, Tokyo 169-8555, Japan.
Dynamic Mode Decomposition (DMD) methods were applied to elementary cellular automata (ECA). An extended DMD method using Walsh functions successfully reproduced ECA dynamics and Koopman eigenvalues, improving upon standard and Hankel DMD techniques.
Area of Science:
- Complex Systems
- Dynamical Systems Theory
- Computational Science
Background:
- Elementary Cellular Automata (ECA) exhibit complex dynamics.
- Dynamic Mode Decomposition (DMD) is a tool for analyzing dynamical systems.
- Koopman eigenvalues characterize the spectral properties of dynamical systems.
Purpose of the Study:
- To investigate the reproducibility of ECA dynamics and Koopman eigenvalues using different DMD methods.
- To develop an improved DMD technique for analyzing ECA.
- To explore the linear-algebraic foundations of DMD reproducibility.
Main Methods:
- Application of standard DMD to ECA time series.
- Implementation of Hankel DMD with delay-embedded time series.
- Development and application of an extended DMD method using nonlinearly transformed time series with discretized Walsh functions.
Main Results:
- Standard DMD showed limitations in reproducing ECA dynamics and Koopman eigenvalues.
- Hankel DMD improved reproducibility but still faced limitations in specific cases.
- The proposed extended DMD method with Walsh functions achieved complete reproduction of dynamics and Koopman eigenvalues.
Conclusions:
- The extended DMD method offers a robust approach for analyzing ECA dynamics.
- Walsh function-based transformations are effective for enhancing DMD in discrete dynamical systems.
- Understanding the linear-algebraic properties is crucial for DMD method development.
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