Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition [PDF]
AIP Advances, 2017 In this letter, we propose a simple and efficient framework of dynamic mode decomposition (DMD) and mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal ...
Yuya Ohmichi
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On Alternative Algorithms for Computing Dynamic Mode Decomposition [PDF]
Computation, 2022 Dynamic mode decomposition (DMD) is a data-driven, modal decomposition technique that describes spatiotemporal features of high-dimensional dynamic data.
Gyurhan Nedzhibov
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Bagging, optimized dynamic mode decomposition for robust, stable forecasting with spatial and temporal uncertainty quantification [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2022 Dynamic mode decomposition (DMD) provides a regression framework for adaptively learning a best-fit linear dynamics model over snapshots of temporal, or spatio-temporal, data. A variety of regression techniques have been developed for producing the linear model approximation whose solutions are exponentials in time.
Diya Sashidhar, J. Nathan Kutz
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PF-DMD: Physics-fusion dynamic mode decomposition for accurate and robust forecasting of dynamical systems with imperfect data and physics [PDF]
, 2023 The DMD (Dynamic Mode Decomposition) method has attracted widespread attention as a representative modal-decomposition method and can build a predictive model. However, the DMD may give predicted results that deviate from physical reality in some scenarios, such as dealing with translation problems or noisy data.
Yin, Yuhui +5 more
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Regularized dynamic mode decomposition algorithm for time sequence predictions [PDF]
Theoretical and Applied Mechanics LettersDynamic mode decomposition (DMD) aims at extracting intrinsic mechanisms in a time sequence via linear recurrence relation of its observables, thereby predicting later terms in the sequence. Stability is a major concern in DMD predictions.
Xiaoyang Xie, Shaoqiang Tang
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Prediction of induced soil vibration during pile vibrodriving using Dynamic Mode Decomposition (DMD) [PDF]
Journal of Physics: Conference SeriesAbstract This study investigates using the Dynamic Mode Decomposition (DMD) algorithm to perform approximations and time-ahead prediction of soil vibrations during the vibrodriving process. Geotechnical applications face challenges in modeling and predicting soil vibrations due to the soil’s heterogeneous nature.
Williams Riquer, Francisco +3 more
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Data-Driven Pulsatile Blood Flow Physics with Dynamic Mode Decomposition [PDF]
Fluids, 2020 Dynamic mode decomposition (DMD) is a purely data-driven and equation-free technique for reduced-order modeling of dynamical systems and fluid flow. DMD finds a best fit linear reduced-order model that represents any given spatiotemporal data.
Milad Habibi +2 more
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Grassmannian Geometry Meets Dynamic Mode Decomposition in DMD-GEN: A New Metric for Mode Collapse in Time Series Generative Models [PDF]
CoRRGenerative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) often fail to capture the full diversity of their training data, leading to mode collapse. While this issue is well-explored in image generation, it remains underinvestigated for time series data.
Amine Mohamed Aboussalah +1 more
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Dynamic mode decomposition using a Kalman filter for parameter estimation [PDF]
AIP Advances, 2018 A novel dynamic mode decomposition (DMD) method based on a Kalman filter is proposed. This paper explains the fast algorithm of the proposed Kalman filter DMD (KFDMD) in combination with truncated proper orthogonal decomposition for many-degree-of ...
Taku Nonomura +2 more
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Quantum Dynamic Mode Decomposition Algorithm for High-Dimensional Time Series Analysis
Intelligent Computing, 2023 The dynamic mode decomposition (DMD) algorithm is a widely used factorization and dimensionality reduction technique in time series analysis. When analyzing high-dimensional time series, the DMD algorithm requires extremely large amounts of computational
Cheng Xue +8 more
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