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cs.LG updates on arXiv.org

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Learning Koopman operators for coupled systems via inform...
Tatsuya Naoi · 2026-05-05 · via cs.LG updates on arXiv.org

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Abstract:Nonlinear coupled systems are ubiquitous in science and engineering. The analysis and modeling of such systems is challenging due to their high dimensionality and complex interactions among subsystems. In recent years, operator-theoretic methods based on the Koopman operator have attracted attention as a powerful tool for analyzing and modeling nonlinear dynamical systems. Extended dynamic mode decomposition (EDMD) is one of the most popular methods to approximate the Koopman operator. However, EDMD is a purely data-driven method, and it could be unstable and inaccurate for coupled systems under limited data availability. In this paper, we propose a method to learn the Koopman operator for coupled systems using the differential equations governing each subsystem. We also demonstrate its effectiveness through numerical experiments on coupled oscillator systems.
Comments: 10 pages, 7 figures
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2605.01835 [cs.LG]
  (or arXiv:2605.01835v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.01835

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Tatsuya Naoi [view email]
[v1] Sun, 3 May 2026 12:03:33 UTC (103 KB)