Time Delay Embeddings - Data-Driven Dynamics | Lecture 5 скачать в хорошем качестве

Time Delay Embeddings - Data-Driven Dynamics | Lecture 5

In this lecture we demonstrate the power of integrating time delay embeddings into the DMD framework. We present two approaches, one based on autoregressive moving averages and the other based on the proper orthogonal decomposition. Both are shown to be simple to implement and can be used to forecast signals that exhibit complex temporal variation. The methods are further demonstrated with MATLAB implementations. Coding demonstration in MATLAB comes from DelayDMD.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.