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Randomized Projection Learning Method for Dynamic Mode Decomposition
Mathematics, 2021 A data-driven analysis method known as dynamic mode decomposition (DMD) approximates the linear Koopman operator on a projected space. In the spirit of Johnson–Lindenstrauss lemma, we will use a random projection to estimate the DMD modes in a reduced ...
Sudam Surasinghe, Erik M. Bollt
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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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DYNAMIC BANDWIDTH VARIATIONAL MODE DECOMPOSITION
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 <p>Signal decomposition techniques aim to break down nonstationary signals into their oscillatory components, serving as a preliminary step in various practical signal processing applications. This has motivated researchers to explore different strategies, yielding several distinct approaches.
Andreas Angelou +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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On dynamic mode decomposition: Theory and applications [PDF]
Journal of Computational Dynamics, 2014 Originally introduced in the fluid mechanics community, dynamic mode decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. However, existing DMD theory deals primarily with sequential time series for which the measurement dimension is much larger than the number of measurements taken.
Dirk M. Luchtenburg +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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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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Extended dynamic mode decomposition for cyclic macroeconomic data
Data Science in Finance and Economics, 2022 We apply methods from the Koopman operator theory, Extended Dynamic Mode Decomposition and machine learning in the study of business cycle models. We use a simple non-linear dynamical system whose main merit is that in the appropriate parameter space ...
John Leventides +2 more
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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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