Results 11 to 20 of about 212,072 (202)
Data-driven MPC with stability guarantees using extended dynamic mode decomposition [PDF]
IEEE Transactions on Automatic Control, 2023 18 pages, 3 ...
Lea Bold +3 more
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A Matlab Toolbox for Extended Dynamic Mode Decomposition Based on Orthogonal Polynomials and p-q Quasi-Norm Order Reduction [PDF]
Mathematics, 2022 Extended Dynamic Mode Decomposition (EDMD) allows an approximation of the Koopman operator to be derived in the form of a truncated (finite dimensional) linear operator in a lifted space of (nonlinear) observable functions.
Camilo Garcia-Tenorio +1 more
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Orthogonal polynomial approximation and Extended Dynamic Mode Decomposition in chaos [PDF]
SIAM Journal on Numerical Analysis, 2023 Extended Dynamic Mode Decomposition (EDMD) is a data-driven tool for forecasting and model reduction of dynamics, which has been extensively taken up in the physical sciences. While the method is conceptually simple, in deterministic chaos it is unclear what its properties are or even what it converges to.
Caroline L. Wormell
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IEEE AccessThis paper presents a novel framework for adaptive learning of Koopman operator to predict the behavior of nonlinear time-varying dynamical systems based on the celebrated extended dynamic mode decomposition (EDMD).
Reiya Asuke, Masahiro Yukawa
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Extended dynamic mode decomposition for model reduction in fluid dynamics simulations [PDF]
Physics of FluidsHigh computational cost and storage/memory requirements of fluid dynamics simulations constrain their usefulness as a predictive tool. Reduced-order models (ROMs) provide a viable solution to this challenge by extracting the key underlying dynamics of a complex system directly from data. We investigate the efficacy and robustness of an extended dynamic
Giulia Libero +3 more
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Group-Convolutional Extended Dynamic Mode Decomposition [PDF]
Physica D: Nonlinear PhenomenaThis paper explores the integration of symmetries into the Koopman-operator framework for the analysis and efficient learning of equivariant dynamical systems using a group-convolutional approach. Approximating the Koopman operator by finite-dimensional surrogates, e.g., via extended dynamic mode decomposition (EDMD), is challenging for high ...
H. Harder +5 more
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Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator [PDF]
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2017 Numerical approximation methods for the Koopman operator have advanced considerably in the last few years. In particular, data-driven approaches such as dynamic mode decomposition (DMD)51 and its generalization, the extended-DMD (EDMD), are becoming increasingly popular in practical applications.
Qianxiao Li +3 more
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On the Effect of Quantization on Extended Dynamic Mode Decomposition [PDF]
2025 American Control Conference (ACC)Extended Dynamic Mode Decomposition (EDMD) is a widely used data-driven algorithm for estimating the Koopman Operator. EDMD extends Dynamic Mode Decomposition (DMD) by lifting the snapshot data using nonlinear dictionary functions before performing the estimation.
Dilip Kumar Maity, Debdipta Goswami
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Extended dynamic mode decomposition with dictionary learning using neural ordinary differential equations [PDF]
Nonlinear Theory and Its Applications, IEICE, 2021 Nonlinear phenomena can be analyzed via linear techniques using operator-theoretic approaches. Data-driven method called the extended dynamic mode decomposition (EDMD) and its variants, which approximate the Koopman operator associated with the nonlinear phenomena, have been rapidly developing by incorporating machine learning methods.
Hiroaki Terao +2 more
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Extended Dynamic Mode Decomposition with Learned Koopman Eigenfunctions for Prediction and Control [PDF]
2020 American Control Conference (ACC), 2020 This paper presents a novel learning framework to construct Koopman eigenfunctions for unknown, nonlinear dynamics using data gathered from experiments. The learning framework can extract spectral information from the full nonlinear dynamics by learning the eigenvalues and eigenfunctions of the associated Koopman operator.
Carl Folkestad +5 more
semanticscholar +7 more sources