Related papers: Learning functions, operators and dynamical systems with kernels

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arXiv Detail & Related papers (2025-09-25T12:00:22Z)
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The Koopman operator provides a principled framework for analyzing nonlinear dynamical systems.<n>VAMPnet and DPNet have been proposed to learn the leading singular subspaces of the Koopman operator.<n>We propose a scalable and conceptually simple method for learning the top-$k$ singular functions of the Koopman operator.
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Learning dynamical systems: an example from open quantum system dynamics [0.0]
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Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces [52.35063796758121]
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Supervised DKRC with Images for Offline System Identification [77.34726150561087]
Modern dynamical systems are becoming increasingly non-linear and complex. There is a need for a framework to model these systems in a compact and comprehensive representation for prediction and control. Our approach learns these basis functions using a supervised learning approach.
arXiv Detail & Related papers (2021-09-06T04:39:06Z)

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