Intrinsic and Extrinsic Approximation of Koopman Operators over Manifolds
Authors: 
Sai Tej Paruchuri, Jia Guo, Michael Kepler, Tim Ryan, Haoran Wang, Andrew J. Kurdila, Daniel StilwellAuthors Info & Claims
Sai Tej Paruchuri, Jia Guo, Michael Kepler, Tim Ryan, Haoran Wang, Andrew J. Kurdila, Daniel StilwellAuthors Info & ClaimsPages 1608 - 1613
Abstract
This paper derives rates of convergence of certain approximations of the Koopman operators that are associated with discrete, deterministic, continuous semiflows on a complete metric space (X,d<inf>X</inf>). Approximations are constructed in terms of reproducing kernel bases that are centered at samples taken along the system trajectory. It is proven that when the samples are dense in a certain type of smooth manifold M ⊆ X, the derived rates of convergence depend on the fill distance of samples along the trajectory in that manifold. Error bounds for projection-based and data-dependent approximations of the Koopman operator are derived in the paper. A discussion of how these bounds are realized in intrinsic and extrinsic approximation methods is given. Finally, a numerical example that illustrates qualitatively the convergence guarantees derived in the paper is given.
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Dec 2020
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Published: 14 December 2020
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