Скачать или смотреть Time Delay Embeddings - Data-Driven Dynamics | Lecture 5

In this lecture, we showcase the power of combining time-delay embeddings with the Dynamic Mode Decomposition (DMD) framework to analyze complex temporal signals. We present two complementary approaches: one based on autoregressive moving averages and the other on proper orthogonal decomposition (POD). Both methods are straightforward to implement and enable accurate forecasting of signals with intricate temporal variation. The lecture is brought to life with MATLAB demonstrations, showing how these techniques can reveal hidden structure and predict the evolution of complex dynamical systems.

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/

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