Results 11 to 20 of about 580,269 (285)
Fast mode decomposition in few-mode fibers [PDF]
Nature Communications, 2020 Characterizing the modes at the output of a multimode fiber is time consuming due to computational cost. Here the authors present an algorithm for few-mode-fiber mode decomposition with a fast processing time and using only intensity measurements.
Egor S. Manuylovich +2 more
doaj +4 more sources
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 ...
Brunton, Steven L. +3 more
core +6 more sources
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
Azencot, Omri +2 more
core +5 more sources
Dynamic mode decomposition with control [PDF]
SIAM Journal on Applied Dynamical Systems, 2014 We develop a new method which extends Dynamic Mode Decomposition (DMD) to incorporate the effect of control to extract low-order models from high-dimensional, complex systems.
Brunton, Steven L. +2 more
core +4 more sources
Challenges in dynamic mode decomposition [PDF]
Journal of The Royal Society Interface, 2021 Dynamic mode decomposition (DMD) is a powerful tool for extracting spatial and temporal patterns from multi-dimensional time series, and it has been used successfully in a wide range of fields, including fluid mechanics, robotics and neuroscience. Two of the main challenges remaining in DMD research are noise sensitivity and issues related to Krylov ...
Ziyou Wu, Steven L. Brunton, Shai Revzen
openaire +3 more sources
Singular Dynamic Mode Decomposition
SIAM Journal on Applied Dynamical Systems, 2023 11 pages. YouTube playlist supporting this manuscript can be found here: https://youtube.com/playlist?list=PLldiDnQu2phsZdFP3nHoGnk_Aq ...
Joel A. Rosenfeld +1 more
openaire +3 more sources
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
openaire +2 more sources
Mode Decomposition Evolution Equations [PDF]
Journal of Scientific Computing, 2011 Partial differential equation (PDE) based methods have become some of the most powerful tools for exploring the fundamental problems in signal processing, image processing, computer vision, machine vision and artificial intelligence in the past two decades. The advantages of PDE based approaches are that they can be made fully automatic, robust for the
Wang, Yang, Wei, Guowei, Yang, Siyang
openaire +4 more sources
Bivariate Empirical Mode Decomposition [PDF]
IEEE Signal Processing Letters, 2007 The empirical mode decomposition (EMD) has been introduced quite recently to adaptively decompose nonstationary and/or nonlinear time series [1]. The method being initially limited to real-valued time series, we propose here an extension to bivariate (or complex-valued) time series that generalizes the rationale underlying the EMD to the bivariate ...
Rilling, Gabriel +3 more
openaire +1 more source
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
openaire +2 more sources