Machine Learning for Computational Fluid Dynamics

Thanks for another great video! As a CFD engineer this is very wholesome :)

I think it's thrilling seeing how ML can be applied to different fields of science, in particular, physics! I'm really interested in learning ML albeit slightly for more hedonistic purposes like high income careers with Data Science, but I always grin and get excited when I see how this booming field is being applied to solving open problems like fluid computation, quantum, and even biology like protein folding. :D

I love watching these videos. Thank you Prof. Steve!

Great video, but the sound is too low. I need to use max volume.

U'r audio is ''low''!! you always blow me away with these! thank's! love this!! so helpful !! 🍌...I don't need 2 use ansys!! good luck!

A great video, thanks very much for your sharing! As a PhD. in fluid dynamics.

Hi,

I am relatively new to this. How can you compare a numerical simulation of a PDE to the exact solution, when you can’t solve the equation and hence don’t know what the exact solution is?

This is the future

oh no steve brunton turned drumheller fountain into a flying saucer

Also, nice video and exciting new research!

Love the explanation and ways of portraying literature in these area. Excited to read that paper. Last few minutes where you talked about benchmarking, reproducible results, and open source code are the keys. Also, to be critical while comparing with state of the art techniques and finding which to use for your problem statement is first step to go ahead with. Really enjoyed the presentation. Thank for sharing.

Thanks for great video

Thank Steve and Ricardo, so impressive to see how ML is applied in fluid dynamics in a systematic way. This is the one area I really want to dig into in my following career (in Ph.D. if possible). Can't wait to read the paper.

GREAT VIDEO!!!

Can you post the link to Rose Yu's seminar at UW?

Finally! The video which I was particularly looking for ❤️

I find this video really giving me the information I was trying to collect these days. Thank you so much! Very beautiful.

Great video. He accelerates a lot my understanding.

Good topic and I love your channel

My CPU got scared...

Just brilliant! Started a month ago my PhD and this video along with your ML Ann. Rev. have just made my background reading a lot easier to get started with. Thank you!

Thanks. It is somewhat frustrating that there are no links in the description to the all the articles mentioned in the video. For example, for 2 articles of Beetham & Capecelatro 2020 i found only 1. Is the 2nd one from 2021?

The reproducibility and sharing the training data is the most important message of this talk

really fascinating...we're exploring the use of ML in micro weather applications (i.e. winds and turbulence in urban canyons)

👏👏👏👍

Bam, 1000th like! :)

Amazing talk, thank you very much for spending the time and for the great delivery!

Volume is too low

Amazing video. Thank you so much both of you

I am so excited your topic that I use cfd to predict chemical process.

Ummmm. This is interesting, but I highly suspect that the ML model used for one specific set of conditions will not properly predict outcomes for other conditions. So, I’m not super sure how actually useful this is in all reality.

Very interesting! What are the tools you are using for your presentation?

This would be great using as a predictor for a higher resolution simulation.

Music cool! Name, please?

insane work

Bravo Steve !!

One video on turbulence model with fourier transform

What is the physical meaning of each POD

OMG why I can almost find one of your video on every the topics I'm interested in/stuying

What is the performance difference between a direct computation and an RNN DNS?

good video

This content displays an impressive depth of insights. A book I read with like-minded themes influenced my path. "The Art of Meaningful Relationships in the 21st Century" by Leo Flint

ML for solving differential equations is totally hype. It can not solve large-scale simulation sizes.

Very nicely presented. One of the best I've seen. I am very interested in learning more about ML for CFD. I have seen some interesting and very promising work on FEA. I have to add a disclaimer here in that I am a CFD software provider for a developer that has integrated a lot of in intelligence in their product, which makes it much faster, easier, while being very accurate. I love what they have done and I am very patiently waiting for AI/ML based CFD to come of age to even further decrease the computing power and provide extremely fast analyses. Keep up the amazing work!

As always very nice and inspiring lecture.

I came here for my undergrad project. Well it's out of my head 😅

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