Sliding Window Dynamic Mode Decomposition - Data-Driven Dynamics | Lecture 4 скачать в хорошем качестве

Sliding Window Dynamic Mode Decomposition - Data-Driven Dynamics | Lecture 4

In this lecture, we explore sliding window Dynamic Mode Decomposition (DMD), a powerful extension of standard DMD designed to overcome its limitations and reveal the structure of multiscale signals. By combining DMD with ideas from the Gabor transform, the method analyzes data in overlapping time windows, allowing us to separate fast and slow timescale dynamics within a complex signal. We illustrate the approach with synthetic data and provide a full coding demonstration, showing how sliding window DMD can uncover hidden patterns and make sense of signals with intricate temporal behaviour. Coding demonstration in MATLAB comes from windowDMD.m here: https://github.com/jbramburger/DataDr... Get the book here: https://epubs.siam.org/doi/10.1137/1.... Scripts and notebooks to reproduce all examples: https://github.com/jbramburger/DataDr... This book provides readers with: methods not found in other texts as well as novel ones developed just for this book; an example-driven presentation that provides background material and descriptions of methods without getting bogged down in technicalities; examples that demonstrate the applicability of a method and introduce the features and drawbacks of their application; and a code repository in the online supplementary material that can be used to reproduce every example and that can be repurposed to fit a variety of applications not found in the book. More information on the instructor: https://hybrid.concordia.ca/jbrambur/ Follow @jbramburger7 on Twitter for updates.