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Moving Target Detection Using Dynamic Mode Decomposition [PDF]
Sensors, 2018 It is challenging to detect a moving target in the reverberant environment for a long time. In recent years, a kind of method based on low-rank and sparse theory was developed to study this problem.
Jingwei Yin +3 more
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Consistent Dynamic Mode Decomposition [PDF]
SIAM Journal on Applied Dynamical Systems, 2019 We propose a new method for computing Dynamic Mode Decomposition (DMD) evolution matrices, which we use to analyze dynamical systems. Unlike the majority of existing methods, our approach is based on a variational formulation consisting of data alignment penalty terms and constitutive orthogonality constraints.
Omri Azencot +2 more
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Beyond expectations: residual dynamic mode decomposition and variance for stochastic dynamical systems [PDF]
Nonlinear dynamics, 2023 Koopman operators linearize nonlinear dynamical systems, making their spectral information of crucial interest. Numerous algorithms have been developed to approximate these spectral properties, and dynamic mode decomposition (DMD) stands out as the ...
Matthew J. Colbrook +3 more
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Molecules, 2023 The Donnan membrane technique (DMT), in which a synthetic or natural solution (the “donor”) is separated from a ligand-free solution (the “acceptor”) by a cation-exchange membrane, is a recognized technique for measuring the concentration of a free metal
Estelle Ricard +3 more
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Residual dynamic mode decomposition: robust and verified Koopmanism [PDF]
Journal of Fluid Mechanics, 2022 Dynamic mode decomposition (DMD) describes complex dynamic processes through a hierarchy of simpler coherent features. DMD is regularly used to understand the fundamental characteristics of turbulence and is closely related to Koopman operators. However,
Matthew J. Colbrook +2 more
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Parametric Dynamic Mode Decomposition for Reduced Order Modeling [PDF]
Journal of Computational Physics, 2022 Dynamic Mode Decomposition (DMD) is a model-order reduction approach, whereby spatial modes of fixed temporal frequencies are extracted from numerical or experimental data sets. The DMD low-rank or reduced operator is typically obtained by singular value
Quincy A. Huhn +3 more
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Determinants of economic growth in East African countries: A dynamic panel model approach
Cogent Economics & Finance, 2023 The purpose of this study is to analyze the determinant of economic growth in the region of East African countries from 2002 to 2018. In order to investigate empirically the key determinants of economic growth in East African countries, this study used a
Muuz Hadush +2 more
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A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition [PDF]
Journal of nonlinear science, 2014 The Koopman operator is a linear but infinite-dimensional operator that governs the evolution of scalar observables defined on the state space of an autonomous dynamical system and is a powerful tool for the analysis and decomposition of nonlinear ...
Matthew O. Williams +2 more
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Bagging, optimized dynamic mode decomposition for robust, stable forecasting with spatial and temporal uncertainty quantification [PDF]
Philosophical Transactions of the Royal Society A, 2021 Dynamic mode decomposition (DMD) provides a regression framework for adaptively learning a best-fit linear dynamics model over snapshots of temporal, or spatio-temporal, data.
Diya Sashidhar, J. Kutz
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Extended Nodal Admittance Matrix Based Stability Analysis of HVDC Connected AC Grids
IEEE Access, 2022 Motivated by emerging HVDC oscillation issues resulted from the interaction with AC grids, impedance-based stability analysis has become an attractive approach in the de-risking studies due to the capability of black-box modelling and clear physical ...
Yonggang Zhang +3 more
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