Textbook in PDF format

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.
Contents :
Front Matter
Dynamic Mode Decomposition：An Introduction
Fluid Dynamics
Koopman Analysis
Video Processing
Multiresolution Dynamic Mode Decomposition
DMD with Control
Delay Coordinates, ERA, and Hidden Markov Models
Noise and Power
Sparsity and DMD
DMD on Nonlinear Observables
Epidemiology
Neuroscience
Financial Trading
Back Matter