Results 31 to 40 of about 13,527,272 (221)
Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis [PDF]
Journal of Fluid Mechanics, 2017 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 ...
A. Towne, O. Schmidt, T. Colonius
semanticscholar +1 more source
Multivariate Dynamic Mode Decomposition and Its Application to Bearing Fault Diagnosis
IEEE Sensors Journal, 2023 In practical engineering applications, the multivariate signal contains more fault feature information than the single-channel signal. How to realize synchronous extraction of fault features from the multivariate signal is of great significance in fault ...
Qixiang Zhang +4 more
semanticscholar +1 more source
A dynamic mode decomposition extension for the forecasting of parametric dynamical systems [PDF]
SIAM Journal on Applied Dynamical Systems, 2021 Dynamic mode decomposition (DMD) has recently become a popular tool for the non-intrusive analysis of dynamical systems. Exploiting Proper Orthogonal Decomposition (POD) as a dimensionality reduction technique, DMD is able to approximate a dynamical ...
Francesco Andreuzzi, N. Demo, G. Rozza
semanticscholar +1 more source
Harčova Nauka ì Tehnologìâ, 2023 Processes of sucrose diffusion inside plant fruits and their generalization require special attention, namely theoretical generalization of experimental data and organization of energy-saving production of candied fruits while preserving the quality of ...
I. Huzova, V. Atamanyuk
doaj +1 more source
Dynamic mode decomposition of dynamic MRI for assessment of pulmonary ventilation and perfusion
Magnetic Resonance in Medicine, 2023 To introduce dynamic mode decomposition (DMD) as a robust alternative for the assessment of pulmonary functional information from dynamic non‐contrast‐enhanced acquisitions.
E. Ilıcak +4 more
semanticscholar +1 more source
Deep Learning Enhanced Dynamic Mode Decomposition [PDF]
Chaos, 2021 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.
C. Curtis, D. J. Alford-Lago, Opal Issan
semanticscholar +1 more source
A characteristic dynamic mode decomposition [PDF]
Theoretical and Computational Fluid Dynamics, 2019 Temporal or spatial structures are readily extracted from complex data by modal decompositions like Proper Orthogonal Decomposition (POD) or Dynamic Mode Decomposition (DMD). Subspaces of such decompositions serve as reduced order models and define either spatial structures in time or temporal structures in space.
Sesterhenn, Jörn, Shahirpour, Amir
openaire +2 more sources
Tensor Train Based Higher Order Dynamic Mode Decomposition for Dynamical Systems
Social Science Research Network, 2023 Higher-order dynamic mode decomposition (HODMD) has proved to be an efficient tool for the analysis and prediction of complex dynamical systems described by data-driven models.
Keren Li, S. Utyuzhnikov
semanticscholar +1 more source
Port-Hamiltonian Dynamic Mode Decomposition [PDF]
SIAM Journal on Scientific Computing, 2022 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
R. Morandin, Jonas Nicodemus, B. Unger
semanticscholar +1 more source
Dynamic mode decomposition of numerical and experimental data
Journal of Fluid Mechanics, 2008 The description of coherent features of fluid flow is essential to our understanding of fluid-dynamical and transport processes. A method is introduced that is able to extract dynamic information from flow fields that are either generated by a (direct ...
P. Schmid, P. Ecole
semanticscholar +1 more source