Results 31 to 40 of about 580,269 (285)
Mode decomposition and renormalization in semiclassical gravity [PDF]
, 1999 We compute the influence action for a system perturbatively coupled to a linear scalar field acting as the environment. Subtleties related to divergences that appear when summing over all the modes are made explicit and clarified. Being closely connected
A. Campos +19 more
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Battery Life Prediction Based on a Hybrid Support Vector Regression Model
Frontiers in Energy Research, 2022 An accurate state of health and remaining useful life prediction is important to provide effective judgment for the lithium-ion battery and reduce the probability of battery effectiveness.
Yuan Chen +3 more
doaj +1 more source
Accuracy of Holographic Real-Time Mode Decomposition Methods Used for Multimode Fiber Laser Emission
Photonics, 2023 Mode decomposition is a powerful tool for analyzing the modal content of optical multimode radiation. There are several basic principles on which this tool can be implemented, including near-field intensity analysis, machine learning, and spatial ...
Denis S. Kharenko +5 more
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Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression
, 2020 Dynamic Mode Decomposition (DMD) yields a linear, approximate model of a system's dynamics that is built from data. We seek to reduce the order of this model by identifying a reduced set of modes that best fit the output.
Graff, John +3 more
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Nonlinear model order reduction via Dynamic Mode Decomposition [PDF]
, 2016 We propose a new technique for obtaining reduced order models for nonlinear dynamical systems. Specifically, we advocate the use of the recently developed Dynamic Mode Decomposition (DMD), an equation-free method, to approximate the nonlinear term.
Alla, Alessandro, Kutz, J. Nathan
core +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 +3 more sources
Energy Science & Engineering, 2023 Global carbon dioxide emissions have become a great threat to economic sustainability and human health. The carbon market is recognized as the most promising mean to curb carbon emissions, furthermore, carbon price forecasting will promote the role of ...
Po Yun +3 more
doaj +1 more source
Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis [PDF]
, 2018 We consider the frequency domain form of proper orthogonal decomposition (POD) called spectral proper orthogonal decomposition (SPOD). Spectral POD is derived from a space-time POD problem for statistically stationary flows and leads to modes that each ...
Colonius, Tim +2 more
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Not-So-Normal Mode Decomposition [PDF]
Physical Review Letters, 2008 4 pages, 2 figures, published ...
openaire +4 more sources
International Journal of Distributed Sensor Networks, 2018 The bidimensional empirical mode decomposition algorithm is more suitable to handle image fusion than the traditional multi-scale decomposition methods in the image fusion area.
Pei Wang, Hui Fu, Ke Zhang
doaj +1 more source