Reference health

Deep learning enhanced dynamic mode decomposition

https://doi.org/10.1063/5.0073893
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23/23 checkable references clean · checked 2026-07-25

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

11 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 23 checked references that resolve
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The analysis of observed chaotic data in physical systems
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Machine Learning for Fluid Mechanics
resolves10.1029/2018SW002061
The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting
resolves10.1073/pnas.17.5.315
Hamiltonian Systems and Transformation in Hilbert Space
resolves10.1137/130932715
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
resolves10.2514/1.J056060
Modal Analysis of Fluid Flows: An Overview
resolves10.1017/S0022112010001217
Dynamic mode decomposition of numerical and experimental data
resolves10.1007/s11071-005-2824-x
Spectral Properties of Dynamical Systems, Model Reduction and Decompositions
resolves10.1007/s00332-015-9258-5
A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition
resolves10.3934/jcd.2015005
A kernel-based method for data-driven koopman spectral analysis
resolves10.1073/pnas.1517384113
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
resolves10.1155/2018/6010634
Applied Koopman Theory for Partial Differential Equations and Data‐Driven Modeling of Spatio‐Temporal Systems
resolves10.1063/1.4993854
Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator
resolves10.1137/17M115414X
Scalable Extended Dynamic Mode Decomposition Using Random Kernel Approximation
resolves10.1063/1.4772195
Applied Koopmanism
resolves10.1137/17M116207X
On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions
resolves10.1038/s41467-018-07210-0
Deep learning for universal linear embeddings of nonlinear dynamics
resolves10.52843/cassyni.ssx9k7
Deep Learning of Conjugate Mappings
resolves10.1073/pnas.1906995116
Data-driven discovery of coordinates and governing equations
resolves10.1038/s41467-017-02388-1
VAMPnets for deep learning of molecular kinetics
resolves10.1007/s00332-019-09598-5
Spectrum of the Koopman Operator, Spectral Expansions in Functional Spaces, and State-Space Geometry
resolves10.1137/17M1125236
Ergodic Theory, Dynamic Mode Decomposition, and Computation of Spectral Properties of the Koopman Operator
resolves10.1038/s41467-017-00030-8
Chaos as an intermittently forced linear system
The 11 references without a DOI — listed, not checked
no DOI — not checkedDynamic Mode Decomposition: Data-Driven Modeling of Complex Systems
no DOI — not checked2023080921151786500_c15
no DOI — not checked2023080921151786500_c16
no DOI — not checked2023080921151786500_c22
no DOI — not checked2023080921151786500_c25
no DOI — not checked2023080921151786500_c26
no DOI — not checked2023080921151786500_c27
no DOI — not checkedChaos, Fractals, and Noise
no DOI — not checkedE. Gonzalez, M. Abudia, M. Jury, R. Kamalapurkar, and J. A. Rosenfeld, “Anti-Koopmanism,” arXiv:2106.00106 (2021).
no DOI — not checkedManifolds, Tensor Analysis, and Applications
no DOI — not checked2023080921151786500_c35
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