English

The kernel perspective on dynamic mode decomposition

Functional Analysis 2023-04-19 v3 Machine Learning Systems and Control Systems and Control

Abstract

This manuscript revisits theoretical assumptions concerning dynamic mode decomposition (DMD) of Koopman operators, including the existence of lattices of eigenfunctions, common eigenfunctions between Koopman operators, and boundedness and compactness of Koopman operators. Counterexamples that illustrate restrictiveness of the assumptions are provided for each of the assumptions. In particular, this manuscript proves that the native reproducing kernel Hilbert space (RKHS) of the Gaussian RBF kernel function only supports bounded Koopman operators if the dynamics are affine. In addition, a new framework for DMD, that requires only densely defined Koopman operators over RKHSs is introduced, and its effectiveness is demonstrated through numerical examples.

Keywords

Cite

@article{arxiv.2106.00106,
  title  = {The kernel perspective on dynamic mode decomposition},
  author = {Efrain Gonzalez and Moad Abudia and Michael Jury and Rushikesh Kamalapurkar and Joel A. Rosenfeld},
  journal= {arXiv preprint arXiv:2106.00106},
  year   = {2023}
}
R2 v1 2026-06-24T02:40:58.216Z