Data-Driven modeling for Li-ion battery using dynamic mode decomposition
Alexandria Engineering Journal, 2022 Lithium-ion (Li-ion) batteries are the workhorse of energy storage systems in electric vehicles (EVs) due to their high energy density and desirable characteristics.
Mohamed A. Abu-Seif +4 more
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Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis [PDF]
, 2018 We consider the frequency domain form of proper orthogonal decomposition (POD) called spectral proper orthogonal decomposition (SPOD). Spectral POD is derived from a space-time POD problem for statistically stationary flows and leads to modes that each ...
Colonius, Tim +2 more
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Multiresolution Dynamic Mode Decomposition [PDF]
SIAM Journal on Applied Dynamical Systems, 2016 We demonstrate that the integration of the recently developed dynamic mode decomposition (DMD) with a multiresolution analysis allows for a decomposition method capable of robustly separating complex systems into a hierarchy of multiresolution time-scale components.
Xing Fu +2 more
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Dynamic Mode Decomposition Based Video Shot Detection
IEEE Access, 2018 Shot detection is widely used in video semantic analysis, video scene segmentation, and video retrieval. However, this is still a challenging task, due to the weak boundary and a sudden change in brightness or foreground objects.
Chongke Bi +6 more
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Learning to Optimize with Dynamic Mode Decomposition
2022 International Joint Conference on Neural Networks (IJCNN), 2022 Designing faster optimization algorithms is of ever-growing interest. In recent years, learning to learn methods that learn how to optimize demonstrated very encouraging results. Current approaches usually do not effectively include the dynamics of the optimization process during training.
Šimánek, Petr +2 more
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Identification of Linear Time-Invariant Systems with Dynamic Mode Decomposition
Mathematics, 2022 Dynamic mode decomposition (DMD) is a popular data-driven framework to extract linear dynamics from complex high-dimensional systems. In this work, we study the system identification properties of DMD.
Jan Heiland, Benjamin Unger
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On the correspondence between Koopman mode decomposition, resolvent mode decomposition, and invariant solutions of the Navier-Stokes equations [PDF]
, 2016 The relationship between Koopman mode decomposition, resolvent mode decomposition and exact invariant solutions of the Navier-Stokes equations is clarified.
McKeon, Beverley J. +2 more
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A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition [PDF]
, 2014 The Koopman operator is a linear but infinite dimensional operator that governs the evolution of scalar observables defined on the state space of an autonomous dynamical system, and is a powerful tool for the analysis and decomposition of nonlinear ...
Kevrekidis, Ioannis G. +2 more
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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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Tensor Train-Based Higher-Order Dynamic Mode Decomposition for Dynamical Systems
Mathematics, 2023 Higher-order dynamic mode decomposition (HODMD) has proved to be an efficient tool for the analysis and prediction of complex dynamical systems described by data-driven models.
Keren Li, Sergey Utyuzhnikov
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