Results 31 to 40 of about 43,720 (247)
Dynamic mode decomposition of the geomagnetic field over the last two decades
Earth and Planetary Physics, 2023 Earth's magnetic field, which is generated in the liquid outer core through the dynamo action, undergoes changes on timescales of a few years to several million years, yet the underlying mechanisms responsible for the field variations remain to be ...
JuYuan Xu, YuFeng Lin
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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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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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The kernel perspective on dynamic mode decomposition
Trans. Mach. Learn. Res., 2021 This manuscript revisits theoretical assumptions concerning dynamic mode decomposition (DMD) of Koopman operators, including the existence of lattices of eigenfunctions, common eigenfunctions between Koopman operators, and boundedness and compactness of Koopman operators.
Efrain Gonzalez +4 more
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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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Dynamic mode decomposition of numerical data in natural circulation
Brazilian Journal of Radiation Sciences, 2021 Dynamic mode decomposition (DMD) has been used for experimental and numerical data analysis in fluid dynamics. Despite of its advantages, the application of the DMD methodology to investigate the natural circulation in nuclear reactors are very scarce in
José Luiz Horacio Faccini
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Stochastic Parameterization with Dynamic Mode Decomposition
, 2022 AbstractA physical stochastic parameterization is adopted in this work to account for the effects of the unresolved small-scale on the large-scale flow dynamics. This random model is based on a stochastic transport principle, which ensures a strong energy conservation. The dynamic mode decomposition (DMD) is performed on high-resolution data to learn a
Li, Long +2 more
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Delay-Embedding Spatio-Temporal Dynamic Mode Decomposition
MathematicsSpatio-temporal dynamic mode decomposition (STDMD) is an extension of dynamic mode decomposition (DMD) designed to handle spatio-temporal datasets. It extends the framework so that it can analyze data that have both spatial and temporal variations.
Gyurhan Nedzhibov
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Automatic Seizure Detection Using Multi-Resolution Dynamic Mode Decomposition
IEEE Access, 2019 Epilepsy is one of the most prevalent neurological issues faced by a large population around the globe. Epilepsy is marked by intermittent seizures, the detection of which can be a challenging problem.
Muhammad Bilal +5 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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