Scalable Extended Dynamic Mode Decomposition Using Random Kernel Approximation

DOI10.1137/17M115414XzbMath1422.37060arXiv1710.10256OpenAlexW2766560513WikidataQ127900912 ScholiaQ127900912MaRDI QIDQ5230606

Nathan M. Urban, Anthony M. Degennaro

Publication date: 28 August 2019

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1710.10256

zbMATH Keywords

system identificationmodel inferencelow-dimensional modelingdynamic mode decompositionscalable algorithms

Mathematics Subject Classification ID

Inference from stochastic processes and spectral analysis (62M15) Time series analysis of dynamical systems (37M10)

Related Items (3)

Extension of dynamic mode decomposition for dynamic systems with incomplete information based on t-model of optimal prediction ⋮ Ensemble forecasts in reproducing kernel Hilbert space family ⋮ Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces

Uses Software

• ImageNet
• AlexNet

Cites Work

• Compressed sensing and dynamic mode decomposition
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• A Computational Method to Extract Macroscopic Variables and Their Dynamics in Multiscale Systems
• On the use of Fourier averages to compute the global isochrons of (quasi)periodic dynamics
• Applied Koopmanism
• Dynamic mode decomposition of numerical and experimental data
• Spectral analysis of nonlinear flows
• Hamiltonian Systems and Transformation in Hilbert Space
• Analysis of Fluid Flows via Spectral Properties of the Koopman Operator
• Linearly Recurrent Autoencoder Networks for Learning Dynamics
• Advanced Lectures on Machine Learning
• Nonlinear Dimensionality Reduction
• Generalized Inverses of Partitioned Matrices

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