Results 11 to 20 of about 40,631 (197)
Large Eddy Simulation and Dynamic Mode Decomposition of Turbulent Mixing Layers
Applied Sciences, 2021 Turbulent mixing layers are canonical flow in nature and engineering, and deserve comprehensive studies under various conditions using different methods. In this paper, turbulent mixing layers are investigated using large eddy simulation and dynamic mode
Yuwei Cheng, Qian Chen
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Constrained Dynamic Mode Decomposition
IEEE Transactions on Visualization and Computer Graphics, 2022 Frequency-based decomposition of time series data is used in many visualization applications. Most of these decomposition methods (such as Fourier transform or singular spectrum analysis) only provide interaction via pre- and post-processing, but no means to influence the core algorithm.
Tim, Krake +3 more
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Towards an Adaptive Dynamic Mode Decomposition
Results in Control and Optimization, 2022 Dynamic Mode Decomposition (DMD) is a tool that creates an approximate model from spatio-temporal data. We have developed an architecture of this tool that will adapt to the data from a given problem by leveraging time delay coordinates, projections, and
Mohammad N. Murshed, M. Monir Uddin
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Visualization and selection of Dynamic Mode Decomposition components for unsteady flow
Visual Informatics, 2021 Dynamic Mode Decomposition (DMD) is a data-driven and model-free decomposition technique. It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data.
T. Krake +4 more
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Dynamic-mode decomposition and optimal prediction [PDF]
Physical Review E, 2021 The Dynamic-Mode Decomposition (DMD) is a well established data-driven method of finding temporally evolving linear-mode decompositions of nonlinear time series. Traditionally, this method presumes that all relevant dimensions are sampled through measurement.
Christopher W. Curtis +1 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.
Azencot, Omri +2 more
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Multiresolution Dynamic Mode Decomposition [PDF]
SIAM Journal on Applied Dynamical Systems, 2016 Summary: We demonstrate that the integration of the recently developed dynamic mode decomposition (DMD) with a multiresolution analysis allows for a decomposition method capable of robustly separating complex systems into a hierarchy of multiresolution time-scale components. A one-level separation allows for background (low-rank) and foreground (sparse)
Kutz, J. Nathan +2 more
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Randomized Dynamic Mode Decomposition [PDF]
SIAM Journal on Applied Dynamical Systems, 2019 This paper presents a randomized algorithm for computing the near-optimal low-rank dynamic mode decomposition (DMD). Randomized algorithms are emerging techniques to compute low-rank matrix approximations at a fraction of the cost of deterministic algorithms, easing the computational challenges arising in the area of `big data'. The idea is to derive a
N. Benjamin Erichson +3 more
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Computational and Mathematical Biophysics, 2023 The COVID-19 pandemic recently caused a huge impact on India, not only in terms of health but also in terms of economy. Understanding the spatio-temporal patterns of the disease spread is crucial for controlling the outbreak.
Rana Kanav Singh, Kumari Nitu
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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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