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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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Dynamic mode decomposition of extreme events [PDF]
Nonlinear Processes in GeophysicsMost data-driven methods, among them Dynamic Mode Decomposition (DMD), focus on analysing and reconstructing the average behaviour of a system. However, the primary interest often lies in the anomalous behaviour, known as extreme events.
M. Ann, J. Behrens, J. Sillmann
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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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High dynamic range spatial mode decomposition
Optics Express, 2020 An accurate readout of low-power optical higher-order spatial modes is of increasing importance to the precision metrology community. Mode sensors are used to prevent mode mismatches from degrading quantum and thermal noise mitigation strategies. Direct mode analysis sensors (MODAN) are a promising technology for real-time monitoring of arbitrary ...
A. W. Jones +3 more
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Machine learning enhanced Hankel dynamic-mode decomposition
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2023 While the acquisition of time series has become more straightforward, developing dynamical models from time series is still a challenging and evolving problem domain. Within the last several years, to address this problem, there has been a merging of machine learning tools with what is called the dynamic-mode decomposition (DMD).
Christopher W. Curtis +3 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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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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Bayesian Dynamic Mode Decomposition [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Dynamic mode decomposition (DMD) is a data-driven method for calculating a modal representation of a nonlinear dynamical system, and it has been utilized in various fields of science and engineering. In this paper, we propose Bayesian DMD, which provides a principled way to transfer the advantages of the Bayesian formulation into DMD.
Naoya Takeishi +3 more
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