NN-ResDMD: Learning Koopman Representations for Complex Dynamics with Spectral Residuals

Yuanchao Xu, Kaidi Shao, Nikos K. Logothetis, Zhongwei Shen

Keywords: Koopman operator, data driven dynamical system, dynamic mode decomposition

Abstract:

Analyzing long-term behaviors in high-dimensional nonlinear dynamical systems remains a significant challenge. The Koopman operator framework has emerged as a powerful tool to address this issue by providing a globally linear perspective on nonlinear dynamics. However, existing methods for approximating the Koopman operator and its spectral components, particularly in large-scale systems, often lack robust theoretical guarantees.
Residual Dynamic Mode Decomposition (ResDMD) introduces a spectral residual measure to assess the convergence of the estimated Koopman spectrum, which helps filter out spurious spectral components.
Nevertheless, it depends on pre-computed spectra, thereby inheriting their inaccuracies.
To overcome its limitations, we introduce the Neural Network-ResDMD (NN-ResDMD), a method that directly estimates Koopman spectral components by minimizing the spectral residual. By leveraging neural networks, NN-ResDMD automatically identifies the optimal basis functions of the Koopman invariant subspace, eliminating the need for manual selection and improving the reliability of the analysis.
Experiments on physical and biological systems demonstrate that NN-ResDMD significantly improves both accuracy and scalability, making it an effective tool for analyzing complex dynamical systems.

Primary Area: learning on time series and dynamical systems

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Submission Number: 7305