The integration of data and scientific computation is driving a paradigm shift across the engineering, natural, and physical sciences. Indeed, there exists an unprecedented availability of high-fidelity measurements from time-series recordings, numerical simulations, and experimental data. When coupled with readily available algorithms and innovations in machine (statistical) learning, it is possible to extract meaningful spatio-temporal patterns that dominate dynamic activity. The focus of this book is on the emerging method of dynamic mode decomposition (DMD). DMD is a matrix decomposition technique that is highly versatile and builds upon the power of the singular value decomposition (SVD). The low-rank structures extracted from DMD are associated with temporal features as well as correlated spatial activity, thus providing a powerful diagnostic for state estimation, model building, control and prediction.
%0 Book
%1 kutz2017dynamic
%A Kutz, Jose Nathan
%A Brunton, Steven L.
%A Brunton, Bingni W.
%A Proctor, Joshua L.
%D 2016
%I SIAM
%K 37m10-time-series-analysis-of-dynamical-systems 37n10-dynamical-systems-in-fluid-mechanics-oceanography-fluid-mechanics 65f20-overdetermined-systems-pseudoinverses 65p99-numerical-problems-in-dynamical-systems dmd
%T Dynamic Mode Decomposition : Data-Driven Modeling of Complex Systems
%U http://www.dmdbook.com/
%X The integration of data and scientific computation is driving a paradigm shift across the engineering, natural, and physical sciences. Indeed, there exists an unprecedented availability of high-fidelity measurements from time-series recordings, numerical simulations, and experimental data. When coupled with readily available algorithms and innovations in machine (statistical) learning, it is possible to extract meaningful spatio-temporal patterns that dominate dynamic activity. The focus of this book is on the emerging method of dynamic mode decomposition (DMD). DMD is a matrix decomposition technique that is highly versatile and builds upon the power of the singular value decomposition (SVD). The low-rank structures extracted from DMD are associated with temporal features as well as correlated spatial activity, thus providing a powerful diagnostic for state estimation, model building, control and prediction.
%@ 9781611974492 1611974496
@book{kutz2017dynamic,
abstract = {The integration of data and scientific computation is driving a paradigm shift across the engineering, natural, and physical sciences. Indeed, there exists an unprecedented availability of high-fidelity measurements from time-series recordings, numerical simulations, and experimental data. When coupled with readily available algorithms and innovations in machine (statistical) learning, it is possible to extract meaningful spatio-temporal patterns that dominate dynamic activity. The focus of this book is on the emerging method of dynamic mode decomposition (DMD). DMD is a matrix decomposition technique that is highly versatile and builds upon the power of the singular value decomposition (SVD). The low-rank structures extracted from DMD are associated with temporal features as well as correlated spatial activity, thus providing a powerful diagnostic for state estimation, model building, control and prediction. },
added-at = {2020-09-03T05:50:39.000+0200},
author = {Kutz, Jose Nathan and Brunton, Steven L. and Brunton, Bingni W. and Proctor, Joshua L.},
biburl = {https://www.bibsonomy.org/bibtex/2d6ead0b92b9de7af388ecf16f963a6ea/gdmcbain},
interhash = {c7297a6e80c1d054403bc355ee8d9d8a},
intrahash = {d6ead0b92b9de7af388ecf16f963a6ea},
isbn = {9781611974492 1611974496},
keywords = {37m10-time-series-analysis-of-dynamical-systems 37n10-dynamical-systems-in-fluid-mechanics-oceanography-fluid-mechanics 65f20-overdetermined-systems-pseudoinverses 65p99-numerical-problems-in-dynamical-systems dmd},
publisher = {SIAM},
refid = {1063563917},
timestamp = {2023-08-09T01:04:11.000+0200},
title = {Dynamic Mode Decomposition : Data-Driven Modeling of Complex Systems},
url = {http://www.dmdbook.com/},
year = 2016
}