DMD Explained! (Dynamic Mode Decomposition)

Want to know what Dynamic Mode Decompositions are? This video gives an introduction to dynamic mode decomposition (DMD) in signal processing. We compare the method with other methods, such as Fourier Series and Principle Component Analysis (PCA), where we see that DMD gives custom data driven bases for your signal, while also exploiting the dynamic content of your signal.

Music:
Come 2gether by Ooyy
Video Call from Los Angeles by Trevor Kowalski
Dismantle by Peter Sandberg
Wrong by Dan Henig
Guardians + Tek by Craig Hardgrove

0:00 Start and Introduction
1:26 DMD Algorithm
2:12 Fourier Series Discussion
3:54 PCA Discussion
5:12 Decompositions for Linear Systems (The Goal)
9:06 Koopman Operators and DMD
13:57 Wrap up

[teegnas]

Earlier this year I was working on Dynamic Mode Decomposition ... and I had a lot of unanswered questions ... and the videos uploaded in this channel over the past month ... have been the answer that I was looking for back then. Thanks for uploading!

[Xisiqomelir]

Instant subscribe, please keep making content! The deeper I go into AIML the more I want to broaden and deepen my fundamental mathematics knowledge, this is good stuff.

[HIMANSHUSINGHvnm]

waiting for something like this!!! thank you 🙏

[arupanshuman]

I have a small clarification to make at 11:40 of the video. Sorry I am unable to work it out. I am getting it as K_F^i(phi(x0)) = (phi(F^i(X0))

[uchennaogunka]

Thank you for the video. Please, can DMD be done for spatial data at only one time (i.e t =1)?

[TriThom]

Can you please explain why do we even care about all these different permutations of DMD? Like what exactly are the issues that are faced with the typical versions of eDMD and DMD that need extensions?