Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems

Data-driven dynamical systems is a burgeoning field—it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning.

Year:2016

Edition:1

Publisher:SIAM-Society for Industrial and Applied Mathematics

Language:English

Pages:241

ISBN 10:1611974496

ISBN 13:9781611974492

File:PDF, 24.28 MB