%0 Book Section %1 vega2021chapter %A Vega, José M. %A Le Clainche, Soledad %B Higher Order Dynamic Mode Decomposition and Its Applications %D 2021 %I Academic Press %K 62-07-data-analysis 62m10-time-series-auto-correlation-regression hodmd %P 1-28 %R https://doi.org/10.1016/B978-0-12-819743-1.00008-2 %T Chapter 1 - General introduction and scope of the book %U https://www.sciencedirect.com/science/article/pii/B9780128197431000082 %X This chapter addresses some preliminary data processing tools that are needed in the remainder of the book. These tools include singular value decomposition in its various versions (economy, compact, and truncated singular value decomposition), proper orthogonal decomposition, and higher order singular value decomposition in its various versions (economy, compact, and truncated higher order singular value decomposition). These tools are illustrated with some toy models. An introduction to reduced order models, both data-driven and projection-based, is also included. The organization of the book is summarized at the end of the chapter. Some selected practice problems are proposed in Annex 1.1. Some MATLAB functions are also given in Annex 1.2 that allow for computing the various versions of singular value decomposition and higher order singular value decomposition. %& 1 %@ 978-0-12-819743-1

@incollection{vega2021chapter, abstract = {This chapter addresses some preliminary data processing tools that are needed in the remainder of the book. These tools include singular value decomposition in its various versions (economy, compact, and truncated singular value decomposition), proper orthogonal decomposition, and higher order singular value decomposition in its various versions (economy, compact, and truncated higher order singular value decomposition). These tools are illustrated with some toy models. An introduction to reduced order models, both data-driven and projection-based, is also included. The organization of the book is summarized at the end of the chapter. Some selected practice problems are proposed in Annex 1.1. Some MATLAB functions are also given in Annex 1.2 that allow for computing the various versions of singular value decomposition and higher order singular value decomposition.}, added-at = {2021-07-20T04:57:58.000+0200}, author = {Vega, José M. and {Le Clainche}, Soledad}, biburl = {https://www.bibsonomy.org/bibtex/200f68d97e40ae79e99c06a77bccbf50a/gdmcbain}, booktitle = {Higher Order Dynamic Mode Decomposition and Its Applications}, chapter = 1, description = {Circulated by sam 2021-07-20}, doi = {https://doi.org/10.1016/B978-0-12-819743-1.00008-2}, interhash = {cb3f72887f6ef3fea9904bde54da3ed4}, intrahash = {00f68d97e40ae79e99c06a77bccbf50a}, isbn = {978-0-12-819743-1}, keywords = {62-07-data-analysis 62m10-time-series-auto-correlation-regression hodmd}, pages = {1-28}, publisher = {Academic Press}, timestamp = {2021-07-20T04:58:33.000+0200}, title = {Chapter 1 - General introduction and scope of the book}, url = {https://www.sciencedirect.com/science/article/pii/B9780128197431000082}, year = 2021 }