On Reduced Input-Output Dynamic Mode Decomposition [PDF]
Advances in Computational Mathematics, 2017 The identification of reduced-order models from high-dimensional data is a challenging task, and even more so if the identified system should not only be suitable for a certain data set, but generally approximate the input-output behavior of the data ...
Benner, Peter +2 more
core +4 more sources
Camera-Based Dynamic Vibration Analysis Using Transformer-Based Model CoTracker and Dynamic Mode Decomposition [PDF]
SensorsAccelerometers are commonly used to measure vibrations for condition monitoring in mechanical and civil structures; however, their high cost and point-based measurement approach present practical limitations.
Liangliang Cheng +4 more
doaj +2 more sources
Dynamic mode decomposition for multiscale nonlinear physics
Physical Review E, 2019 We present a data-driven method for separating complex, multiscale systems into their constituent time-scale components using a recursive implementation of dynamic mode decomposition (DMD). Local linear models are built from windowed subsets of the data, and dominant time scales are discovered using spectral clustering on their eigenvalues.
Molei Tao +2 more
openaire +6 more sources
Dynamic Mode Decomposition of Fast Pressure Sensitive Paint Data [PDF]
Sensors, 2016 Fast-response pressure sensitive paint (PSP) is used in this work to measure and analyze the acoustic pressure field in a rectangular cavity. The high spatial resolution and fast frequency response of PSP effectively captures the spatial and temporal ...
Mohd Y. Ali +2 more
doaj +2 more sources
Stochastic Parameterization with Dynamic Mode Decomposition
, 2022 AbstractA physical stochastic parameterization is adopted in this work to account for the effects of the unresolved small-scale on the large-scale flow dynamics. This random model is based on a stochastic transport principle, which ensures a strong energy conservation. The dynamic mode decomposition (DMD) is performed on high-resolution data to learn a
Li, Long +2 more
openaire +3 more sources
Identification of dynamic textures using Dynamic Mode Decomposition
IFAC-PapersOnLine, 2020 Abstract Dynamic Textures (DTs) are image sequences of moving scenes that present stationary properties in time. In this paper, we apply Dynamic Mode Decomposition (DMD) and Dynamic Mode Decomposition with Control (DMDc) to identify a parametric model of dynamic textures.
Previtali, Davide +3 more
openaire +4 more sources
Deep learning enhanced dynamic mode decomposition [PDF]
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2022 Koopman operator theory shows how nonlinear dynamical systems can be represented as an infinite-dimensional, linear operator acting on a Hilbert space of observables of the system. However, determining the relevant modes and eigenvalues of this infinite-dimensional operator can be difficult.
D. J. Alford-Lago +3 more
openaire +4 more sources
Proper orthogonal and dynamic mode decomposition of sunspot data. [PDF]
Philos Trans A Math Phys Eng Sci, 2021 Albidah AB +7 more
europepmc +2 more sources
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
openaire +4 more sources
Novel data-driven, equation-free method captures spatio-temporal patterns of neurodegeneration in Parkinson's disease: Application of dynamic mode decomposition to PET. [PDF]
Neuroimage Clin, 2020 Fu JF +4 more
europepmc +2 more sources