%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 %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 = {2020-10-21T02:18:10.000+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 = {}, publisher = {SIAM}, refid = {1063563917}, timestamp = {2020-10-21T02:18:10.000+0200}, title = {Dynamic Mode Decomposition : Data-Driven Modeling of Complex Systems}, url = {http://www.dmdbook.com/}, year = 2016 }