Parsimonious Dynamic Mode Decomposition: A Robust and Automated Approach for Optimally Sparse Mode Selection in Complex Systems

• URL: http://arxiv.org/abs/2410.16656v1
• Date: Tue, 22 Oct 2024 03:00:11 GMT
• Title: Parsimonious Dynamic Mode Decomposition: A Robust and Automated Approach for Optimally Sparse Mode Selection in Complex Systems
• Authors: Arpan Das, Pier Marzocca, Oleg Levinski,
• Abstract summary: This paper introduces the Parsimonious Dynamic Mode Decomposition (parsDMD) ParsDMD is a novel algorithm designed to automatically select an optimally sparse subset of dynamic modes for both temporal and purely temporal data. It is validated on a diverse range of datasets, including standing wave signals, identifying hidden dynamics, fluid dynamics simulations, and atmospheric sea-surface temperature (SST) data.
• Score: 0.40964539027092917
• License:
• Abstract: This paper introduces the Parsimonious Dynamic Mode Decomposition (parsDMD), a novel algorithm designed to automatically select an optimally sparse subset of dynamic modes for both spatiotemporal and purely temporal data. By incorporating time-delay embedding and leveraging Orthogonal Matching Pursuit (OMP), parsDMD ensures robustness against noise and effectively handles complex, nonlinear dynamics. The algorithm is validated on a diverse range of datasets, including standing wave signals, identifying hidden dynamics, fluid dynamics simulations (flow past a cylinder and transonic buffet), and atmospheric sea-surface temperature (SST) data. ParsDMD addresses a significant limitation of the traditional sparsity-promoting DMD (spDMD), which requires manual tuning of sparsity parameters through a rigorous trial-and-error process to balance between single-mode and all-mode solutions. In contrast, parsDMD autonomously determines the optimally sparse subset of modes without user intervention, while maintaining minimal computational complexity. Comparative analyses demonstrate that parsDMD consistently outperforms spDMD by providing more accurate mode identification and effective reconstruction in noisy environments. These advantages render parsDMD an effective tool for real-time diagnostics, forecasting, and reduced-order model construction across various disciplines.

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