On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions

DOI10.1137/17M116207XzbMath1408.37010arXiv1712.07144OpenAlexW2963490775WikidataQ129603755 ScholiaQ129603755MaRDI QIDQ4686615

Ioannis G. Kevrekidis, Erik M. Bollt, Qianxiao Li, Felix Dietrich

Publication date: 4 October 2018

Published in: SIAM Journal on Applied Dynamical Systems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1712.07144

zbMATH Keywords

conjugacyrectificationKoopman operatordata-driven algorithmsflow box

Mathematics Subject Classification ID

Dynamics induced by flows and semiflows (37C10) Applications of operator theory to differential and integral equations (47N20) Linear composition operators (47B33) Dynamical systems involving transformations and group actions with special properties (minimality, distality, proximality, expansivity, etc.) (37B05) Algebraic ergodic theory, cocycles, orbit equivalence, ergodic equivalence relations (37A20) Dynamical aspects of statistical mechanics (37A60) Dynamical systems involving relations and correspondences in one complex variable (37F05)

Related Items (8)

Modern Koopman Theory for Dynamical Systems ⋮ Nonlinear observability via Koopman analysis: characterizing the role of symmetry ⋮ Discrete-time nonlinear feedback linearization via physics-informed machine learning ⋮ On the Koopman Operator of Algorithms ⋮ Spectral analysis of the Koopman operator for partial differential equations ⋮ Manifold learning for organizing unstructured sets of process observations ⋮ Geometric considerations of a good dictionary for Koopman analysis of dynamical systems: cardinality, ``primary eigenfunction, and efficient representation ⋮ On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD

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