Machine Learning for Fluid Dynamics: Patterns

This video discusses how machine learning is currently being used to extract useful patterns and coherent structures in high-dimensional fluid dynamics.

Download paper at the Annual Review of Fluid Mechanics:
Follow updates on Twitter @eigensteve

This video was produced at the University of Washington

[UTElistan]

This is exactly how authors should present their papers. I wish every paper on arxiv was presented this way

[luorisluo]

Being a student to Steve must be one of the most amazing thing in the world.

[shoutash]

I’m just getting into ML and seeing these videos as a CFD researcher gets me absolutely excited! Thanks for the great content! :)

[superuser]

Wow. What I love about watching one of these videos for the 1000th time is that I'm just as blown away learning both the history and the math as I was the first time I saw one years ago. Thanks

[amriteshsinha]

I use CFD quite heavily and I can't tell you how much I appreciate this . Wonderful channel and nice explanations.

[Default_]

Just a month ago I was watching his lecture on deriving the Fourier transformation. This is beautifully done

[deiling]

I am so gratefull this is for free and available nowadays

[mcmormus]

Dear Dr. Brunton
i have been watching your videos for some time now for fun and it fills me with joy. Even though I have nothing to do with Deep Learning and Neural Networks (YET), I just wanted to say thank you!
Greetings from Germany.

[kubafrank]

I'm about to start my PhD studying fluid flows around wind and tidal turbines. Never done ML before but this looks like it could be relevant for my future work (I know for a fact that the lab uses PIV measurements) so I'll try to get as much under my belt from this series.

[__--JY-Moe--__]

laser scan in a vacuum, on a vibration table..so now nn's can read right off the presentation..easily understood..thanks...looking 4 a class now!!

[RobertKost]

I learned quickly, just to keep up. Excellent presentation!!

[vikaskushwaha]

I really appreciate the work you have done; the best part is that you came forward and explained your part of the work via visual (i.e. presenting) as it gives a much better idea even for a student like me without going into high-level details. Of course one can read the papers, but as there is a lot of work going on in many groups, in many sectors sometimes it just hard to find the right one for the right person. However, from your paper/visual explanation, I have got a better understanding of how ML can be used in Fluid mechanics.

~ Just thought of a master student 😊

[bluecpp]

This is the true feeling of love!
Thank you for this feeling. ☺️🙏🏻

[sjh]

I love your contents and delivery, Prof. Brunton. But I am wondering could you maybe talk about how might machine learning (or data-driven approach) help with the development of turbulence theory?

[wodemamaya]

Awesome! Steve, you have the talent to make the AI stuff human friendly. lol

[zildjiandrummer]

I'm subscribed. This is great work, and the currently-bloated ML field needs way more distillation to re-contextualize research such as this.

[jacopobilotto]

Thanks for the content you upload. It is highly appreciated

[mattkafker]

Excellent video. Thank you, Professor.

[nikhilm]

Thank you Dr.Brunton for yet another informative video. As mentioned at 16:20, extrapolation in such cases is extremely challenging to do well. In this context I was wondering what your thoughts were regarding ways to incorporate physics models directly into the machine learning / inference pipeline (as the process becomes costlier). A video about this topic would also be very interesting.

[ryanli]

There may be an error in the line chart on 18:31, the horizontal axis represents the mode number instead of the amount of training data. I went to the original article and I think the right figure should be Figure 12 (a) of the original article, instead of Figure 4 that is used now.