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Non-Stationary Dynamic Mode Decomposition
IEEE Access, 2023 Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal ...
John Ferre +4 more
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Robust Dynamic Mode Decomposition
IEEE Access, 2022 This paper develops a robust dynamic mode decomposition (RDMD) method endowed with statistical and numerical robustness. Statistical robustness ensures estimation efficiency at the Gaussian and non-Gaussian probability distributions, including heavy ...
Amir Hossein Abolmasoumi +2 more
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Swarm Modeling With Dynamic Mode Decomposition
IEEE Access, 2022 Modelling biological or engineering swarms is challenging due to the inherently high dimension of the system, despite the often low-dimensional emergent dynamics.
Emma Hansen +2 more
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Real-time motion detection using dynamic mode decomposition [PDF]
EURASIP Journal on Image and Video ProcessingDynamic mode decomposition (DMD) is a numerical method that seeks to fit time-series data to a linear dynamical system. In doing so, DMD decomposes dynamic data into spatially coherent modes that evolve in time according to exponential growth/decay or ...
Marco Mignacca +2 more
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International Journal of Electrical Power & Energy Systems, 2023 A transition to renewable energy is increasing the long-distance export of power, with reduced spinning inertia and small stability margins. In this work, we apply higher-order variants of a data-driven technique, the dynamic mode decomposition (DMD ...
C.N.S. Jones, S.V. Utyuzhnikov
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Phonocardiogram Classification Using Dynamic Mode Decomposition for Heterogeneity-Resilient Training
IEEE Open Journal of Instrumentation and MeasurementMedical measurements of the heart, such as a phonocardiogram (PCG), provide a noninvasive means for diagnosing valvular heart diseases. Recently, deep learning has shown great success in the classification of PCG.
Ebrahim A. Nehary, Sreeraman Rajan
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Regularized dynamic mode decomposition algorithm for time sequence predictions
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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Large Eddy Simulation and Dynamic Mode Decomposition of Turbulent Mixing Layers
Applied Sciences, 2021 Turbulent mixing layers are canonical flow in nature and engineering, and deserve comprehensive studies under various conditions using different methods. In this paper, turbulent mixing layers are investigated using large eddy simulation and dynamic mode
Yuwei Cheng, Qian Chen
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Towards an Adaptive Dynamic Mode Decomposition
Results in Control and Optimization, 2022 Dynamic Mode Decomposition (DMD) is a tool that creates an approximate model from spatio-temporal data. We have developed an architecture of this tool that will adapt to the data from a given problem by leveraging time delay coordinates, projections, and
Mohammad N. Murshed, M. Monir Uddin
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Visualization and selection of Dynamic Mode Decomposition components for unsteady flow
Visual Informatics, 2021 Dynamic Mode Decomposition (DMD) is a data-driven and model-free decomposition technique. It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data.
T. Krake +4 more
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