Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables

DOI10.1016/j.neunet.2019.04.020zbMath1441.37092arXiv1808.10551OpenAlexW2963638622WikidataQ92318059 ScholiaQ92318059MaRDI QIDQ2185599

Yoshinobu Kawahara, Keisuke Fujii

Publication date: 5 June 2020

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1808.10551

zbMATH Keywords

spectral analysisunsupervised learningdimensionality reduction

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and numerical treatment of dynamical systems (37M99)

Related Items (6)

Modern Koopman Theory for Dynamical Systems ⋮ Learning rate of distribution regression with dependent samples ⋮ Distributed learning for sketched kernel regression ⋮ Neural dynamic mode decomposition for end-to-end modeling of nonlinear dynamics ⋮ Unnamed Item ⋮ Linearly Constrained Linear Quadratic Regulator from the Viewpoint of Kernel Methods

Cites Work

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• Synchronization in networks of nonlinear dynamical systems coupled via a directed graph
• Dynamic mode decomposition of numerical and experimental data
• Tensor-based dynamic mode decomposition
• Spectral analysis of nonlinear flows
• Hamiltonian Systems and Transformation in Hilbert Space
• On Learning Vector-Valued Functions
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