Results 11 to 20 of about 17,648 (273)
On the Effect of Quantization on Extended Dynamic Mode Decomposition
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 ...
Maity, Dipankar, Goswami, Debdipta
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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 ...
Libero, Giulia +3 more
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Extended dynamic mode decomposition for two paradigms of non-linear dynamical systems
Journal of the Franklin Institute, 2023 We apply the Koopman operator theory and Extended Dynamic Mode Decomposition in two non-linear dynamical systems. The first one is the 3x+1-system which follows by the so-called Collatz conjecture and it is discrete. The second one is the SIR-model which
Poulios, C., Leventides, J., Melas, E.
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Dictionary learning in Extended Dynamic Mode Decomposition using a reservoir computer [PDF]
, 2019 We aim at improving extended dynamic mode decomposition that allows to linearize nonlinear systems.Indeed, the EDMD algorithm provides a finite-dimensional representation of the Koopman operator.Finally, the reservoir computer is trained to produce an ...
Mauroy, Alexandre; id_orcid +1 more
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Linear Control of a Nonlinear Aerospace System via Extended Dynamic Mode Decomposition
AIAA SCITECH 2022 Forum, 2022 The linear representation of nonlinear systems dynamics has a tremendous potential to enable the estimation, prediction, and control of nonlinear systems using standard and well-known methodologies available for linear systems.
Cartocci N. +11 more
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A concise introduction to Koopman operator theory and the Extended Dynamic Mode Decomposition
CoRRThe framework of Koopman operator theory is discussed along with its connections to Dynamic Mode Decomposition (DMD) and (Kernel) Extended Dynamic Mode Decomposition (EDMD). This paper provides a succinct overview with consistent notation.
Patyn, Christophe, Deconinck, Geert
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2021 22nd IEEE International Conference on Industrial Technology (ICIT), 2021 A novel approach to solving the problem of controlling nonlinear systems is based on the so-called Koopman operator. The Koopman operator is a linear operator that governs the evolution of scalar functions (often referred to as observables) along the ...
Ileš, Šandor +2 more
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Koopman theory is widely used for data-driven modeling of nonlinear dynamical systems. One of the well-known algorithms that stem from this approach is the Extended Dynamic Mode Decomposition (EDMD), a data-driven algorithm for uncontrolled systems.
Tilki, Cankat
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International Journal of Distributed Sensor Networks, 2019 In this work, a state-of-art nonlinear system identification method based on empirical mode decomposition is utilized and extended to detect bolt loosening in a jointed beam.
Chao Xu, Chen-Chen Huang, Wei-Dong Zhu
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Extended dynamic mode decomposition for inhomogeneous problems [PDF]
Journal of Computational Physics, 2021 Dynamic mode decomposition (DMD) is a powerful data-driven technique for construction of reduced-order models of complex dynamical systems. Multiple numerical tests have demonstrated the accuracy and efficiency of DMD, but mostly for systems described by partial differential equations (PDEs) with homogeneous boundary conditions.
Hannah Lu, Daniel M. Tartakovsky
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