Results 11 to 20 of about 1,753,791 (268)
MODULO: A software for Multiscale Proper Orthogonal Decomposition of data
SoftwareX, 2020 In the era of the Big Data revolution, methods for the automatic discovery of regularities in large datasets are becoming essential tools in applied sciences. This article presents an open software package, named MODULO (MODal mULtiscale pOd), to perform
Davide Ninni, Miguel A. Mendez
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Model Order Reduction by Proper Orthogonal Decomposition [PDF]
CoRR, 2019 arXiv admin note: substantial text overlap with arXiv:1701 ...
Carmen Gräßle +2 more
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Proper orthogonal and dynamic mode decomposition of sunspot data [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020 High-resolution solar observations show the complex structure of the magnetohydrodynamic (MHD) wave motion. We apply the techniques of proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) to identify the dominant MHD wave modes in a sunspot using the intensity time series.
A. B. Albidah +7 more
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Kosambi and proper orthogonal decomposition [PDF]
Resonance, 2011 In 1943 Kosambi published a paper titled ‘Statistics in function space’ in the Journal of the Indian Mathematical Society. This paper was the first to propose the technique of statistical analysis often called proper orthogonal decomposition today. This article describes the contents of that paper and Kosambi’s approach to the subject.
Narasimha, Roddam
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ECL BENCHMARK: APPLICATION OF THE PROPER ORTHOGONAL DECOMPOSITION
Mechanical Systems and Signal Processing, 2003 The aim of this paper is to apply the proper orthogonal decomposition method to a non-linear system consisting of an experimental cantilever beam with a geometrical non-linearity.
Lenaerts, Vincent +2 more
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A few thoughts on proper orthogonal decomposition in turbulence
Physics of Fluids, 2017 Proper orthogonal decomposition was originally introduced in turbulence to identify large-scale patterns in turbulent flows. Over the years, several extensions have been formulated in order to strengthen its model-predictive abilities, with limited success in the case of fully developed turbulence.
Podvin, Bérengère, Fraigneau, Yann
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Gappy Spectral Proper Orthogonal Decomposition
SSRN Electronic Journal, 2022 Experimental spatio-temporal flow data often contain gaps or other types of undesired artifacts. To reconstruct flow data in the compromised or missing regions, a data completion method based on spectral proper orthogonal decomposition (SPOD) is developed.
Akhil Nekkanti, Oliver T. Schmidt
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A randomized proper orthogonal decomposition technique [PDF]
2015 American Control Conference (ACC), 2015 In this paper, we consider the problem of model reduction of large scale systems, such as those obtained through the discretization of PDEs. We propose a randomized proper orthogonal decomposition (RPOD) technique to obtain the reduced order models by randomly choosing a subset of the inputs/outputs of the system to construct a suitable small sized ...
Dan Yu, Suman Chakravorty
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Hierarchical Approximate Proper Orthogonal Decomposition [PDF]
SIAM Journal on Scientific Computing, 2018 Proper Orthogonal Decomposition (POD) is a widely used technique for the construction of low-dimensional approximation spaces from high-dimensional input data. For large-scale applications and an increasing number of input data vectors, however, computing the POD often becomes prohibitively expensive.
Christian Himpe +2 more
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Dissipation-optimized proper orthogonal decomposition
Physics of Fluids, 2023 We present a formalism for dissipation-optimized decomposition of the strain rate tensor (SRT) of turbulent flow data using Proper Orthogonal Decomposition (POD). The formalism includes a novel inverse spectral SRT operator allowing the mapping of the resulting SRT modes to corresponding velocity fields, which enables a complete dissipation-optimized ...
P. J. Olesen +4 more
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