On Convergent Dynamic Mode Decomposition and its Equivalence with Occupation Kernel Regression

• Published in: IFAC-PapersOnLine Vol. 58; no. 17; pp. 103 - 108
• Main Authors: Abudia, Moad, Rosenfeld, Joel, Kamalapurkar, Rushikesh
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2024
• Subjects: Machine Learning and Control; Operator Theoretic Methods in Systems Theory; System Identification; System Identification; Machine Learning and Control; Operator Theoretic Methods in Systems Theory
• ISSN: 2405-8963; 2405-8971; 2405-8963
• DOI: 10.1016/j.ifacol.2024.10.121

This paper presents a new technique for norm-convergent dynamic mode decomposition of deterministic systems. The developed method utilizes recent results on singular dynamic mode decomposition where it is shown that by appropriate selection of domain and range Hilbert spaces, the Liouville operator (also known as the Koopman generator) can be made to be compact. In this paper, it is shown that by selecting appropriate collections of finite basis functions in the domain and the range, a novel finite-rank representation of the Liouville operator may be obtained. It is also shown that the model resulting from dynamic mode decomposition of the finite-rank representation is closely related to regularized regression using the so-called occupation kernels as basis functions.