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

In this lecture we learn about sliding window dynamic mode decomposition. We show that the goal of the method is to overcome limitations of standard DMD, while also helping to separate out fast and slow timescale dynamics in a signal. The method is based on the Gabor transform and works by subsampling snapshot data in "windows" to apply DMD to. The method is demonstrated on synthetic data with a full coding demonstration. Coding demonstration in MATLAB comes from windowDMD.m here: https://github.com/jbramburger/DataDrivenDynSyst/tree/main/Linear%20Evolution%20Models Get the book here: https://epubs.siam.org/doi/10.1137/1.9781611978162 Scripts and notebooks to reproduce all examples: https://github.com/jbramburger/DataDrivenDynSyst 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.