Results 41 to 50 of about 47,248 (288)
Port-Hamiltonian Dynamic Mode Decomposition
SIAM Journal on Scientific Computing, 2023 We present a novel physics-informed system identification method to construct a passive linear time-invariant system. In more detail, for a given quadratic energy functional, measurements of the input, state, and output of a system in the time domain, we find a realization that approximates the data well while guaranteeing that the energy functional ...
Riccardo Morandin +2 more
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Comparative study of modal decomposition and dynamic equation reconstruction in data-driven modeling
AIP Advances, 2021 Due to the increasing complexity of dynamic systems, it is increasingly difficult for traditional mathematical methods to meet the modeling requirements of complex dynamic systems.
Zhenglong Yin +4 more
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Higher order dynamic mode decomposition beyond aerospace engineering
Results in Engineering, 2023 It is a well known fact that fluid dynamics play a crucial rule in countless fields in scientific and industrial applications, including nature and medicine (ocean currents, fluid motion around jellyfish, blood circulation...), in energy production (wind
N. Groun +4 more
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Wireless Technology Identification Employing Dynamic Mode Decomposition Modeling
IEEE Access, 2023 Significant growth in broadband wireless services, as well as ever-increasing demand on the spectrum caused by the Internet of Things (IoT) have overstretched limited available spectrum space for wireless services.
Ahmed Elsebaay, Hazem H. Refai
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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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Prediction Accuracy of Dynamic Mode Decomposition
SIAM Journal on Scientific Computing, 2020 Dynamic mode decomposition (DMD), which the family of singular-value decompositions (SVD), is a popular tool of data-driven regression. While multiple numerical tests demonstrated the power and efficiency of DMD in representing data (i.e., in the interpolation mode), applications of DMD as a predictive tool (i.e., in the extrapolation mode) are scarce.
Hannah Lu, Daniel M. Tartakovsky
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EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs [PDF]
, 2014 This paper introduces a new signal-filtering which combines the empirical mode decomposition (EMD) and a similarity measure. A noisy signal is adaptively broken down into oscillatory components called intrinsic mode functions (IMFs) by EMD followed by an
DARE, Delphine +4 more
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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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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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