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Non-Stationary Dynamic Mode Decomposition
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DOI：10.1109/ACCESS.2023.3326412.png)
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摘要
En 中文
Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal structure. Dynamic Mode Decomposition is a means to achieve this goal, allowing the identification of key spatiotemporal modes through the diagonalization of a finite dimensional approximation of the Koopman operator. However, these methods apply best to time-translationally invariant or stationary data, while in many typical cases, dynamics vary across time and conditions. To capture this temporal evolution, we developed a method, Non-Stationary Dynamic Mode Decomposition, that generalizes Dynamic Mode Decomposition by fitting global modulations of drifting spatiotemporal modes. This method accurately predicts the temporal evolution of modes in simulations and recovers previously known results from simpler methods. To demonstrate its properties, the method is applied to multi-channel recordings from an awake behaving non-human primate performing a cognitive task.
Keyword:
Spatiotemporal phenomena
Modulation
Heuristic algorithms
Brain modeling
Visualization
Physiology
Meteorology
Time series analysis
Data models
Neuroscience
Computational neuroscience
Dynamic mode decomposition
non-stationary methods
multi-variate time-series
data-driven modeling
computational neuroscience
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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