Machine Learning for Computational Fluid Dynamics

[@periquitopedro]

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!

[@AlexLiberzon]

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

[@hasnaouiacademy7899]

I am really interrested to this field, I work on turbulence modeling with ML in my PhD. thesis. Thank 's Prof. Steve.

[@JousefM]

Interviewed Steve a while ago, maybe this helps: kzbin.info/www/bejne/navKmJeAaa11aZo

[@MuhammadUzair-w3o]

Hy this is Muhammad Hamza, I am also working on the machine learning approach for computional fluid dynamics, can you shair your thesis with me

[@kingsleyzissou5881]

11:43 With the results presented so far I'm not impressed, because it's always possible to optimize a stencil or WENO scheme for one particular problem. I would be curious to see what these NN based schemes do when presented with new problems. I've yet to see any NN based approach be used as a black box to improve or accelerate CFD calculations. Also, for the interpolation problem, wouldn't any monotonized scheme cure the overshoot issue and be much cheaper to evaluate? How many weights are in that network - how many FLOPs? I guess I need to read the original paper but I don't understand what is so amazing about that.

[@kesav1985]

Bummer! This is so overhyped! It is easy to fool people without core knowledge of CFD.

[@kingsleyzissou5881]

@@kesav1985 Indeed

[@cambridgebreaths3581]

Hi Steve, can you please recommend the essential videos (in a systematic way) of yours in this channel that are a prerequisite to watch prior to understand this paper in full. Thanks a lot

[@hasnaouiacademy7899]

I suggest ''Machine Learning for Fluid Mechanics" by Prof. Steve et al. It's verry useful to understand ML, even if you are a computer science engineer.

[@JousefM]

Suggested Paper: www.annualreviews.org/doi/abs/10.1146/annurev-fluid-010719-060214 Shameless plug from my side: kzbin.info/www/bejne/navKmJeAaa11aZo - interview with Steve :)

[@Virtura3D]

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!

[@梅川王绔子]

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.

[@ricardovinuesam]

Thank you!! :)

[@utente1854]

I am sorry to raise some criticism, Prof. Brunton, I am an old CFD engineer with some experience in development and industrial applications. As a novice to ML I feel a bit disoriented, I went through the paper of Kochkov, that of Sinai, and honestly, some of the things look to me completely pointless. At 7:21 there is DNS on a coarse mesh, that needs to be trained on the fly, using a DNS for the same test case on a high resolution mesh. Does it make any sense?? Likewise, at 8:55 I can see the Burgers'equation accurately described by the neural interpolator. But can we apply that same learned model for another equation and having the same accuracy? Turbulence modeling also is questionable, and many important CFD groups seem to have already ababndoned the idea. The only part which seems very interesting is the POD, but it is not obvious to me how this could be transferred to industry heavily relying on CFD (steady RANS, URANS). Sorry for the naive comment.

[@liuyq4856]

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

[@AD-ox4ng]

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!

[@iheavense]

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

[@apocalypt0723]

Amazing video. Thank you so much both of you

[@yuchenma3102]

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

[@withawintvil]

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

[@HamidReza-vl2oj]

As always very nice and inspiring lecture.

[@kirilangelov9752]

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

[@diegoandrade3912]

Bravo Steve !!

[@shuvranilsanyal1018]

Finally! The video which I was particularly looking for ❤️

[@ru2yaz33]

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

[@jti107]

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

[@Jibs-HappyDesigns-990]

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!

[@JaydeepSinghTindori]

Great video. He accelerates a lot my understanding.

[@hokhay]

Good topic and I love your channel

[@AMADEOSAM]

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

[@Bill0102]

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

[@oliviertelemaque8729]

insane work

[@vitorbortolin6810]

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

[@siennathesane]

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

[@wilmomontero5017]

GREAT VIDEO!!!

[@pauloyoshiokubota5208]

Thanks for great video

[@kylebeggs2617]

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

[@Eigensteve]

Thanks for the reminder! Here it is: kzbin.info/www/bejne/nmi3l3mpqKd5e9U

[@kylebeggs2617]

@@Eigensteve Wow, quick reply! Thank you for putting in the hard work to make these videos. They are magnificent!

[@RomanSheinman]

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?

[@hindswraj4883]

One video on turbulence model with fourier transform

[@happyfrog666]

Cheers Steve

[@fisica_altas_energias]

Music cool! Name, please?

[@mathurnil4616]

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.

[@randomguy7658]

What is the physical meaning of each POD

[@otheraccount5252]

oh no steve brunton turned drumheller fountain into a flying saucer Also, nice video and exciting new research!

[@dipesh1dp]

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

[@robinking9279]

good video

[@airman122469]

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.

[@dj-maxus]

some of the mentioned methods (such as SINDy) are meant to produce models of stable predictions beyond training conditions

[@ilpreterosso]

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

[@steveshaver4000]

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?

[@kingsleyzissou5881]

People use the method of manufactured solutions for this sometimes. You specify the solution (satisfying IC/BCs) beforehand, compute the differential operators based on this solution, and then include the result as a source term of the PDE. This only tells you that you are solving the PDE correctly, it does not tell you that your PDE + chosen parameters are a proper fit for the physics.

[@baronfillpot]

This is the future

[@JousefM]

Bam, 1000th like! :)

[@Chiavaccio]

👏👏👏👍

[@ARkhan-xw8ud]

Volume is too low

[@Infofirefree]

1:53

[@MarkMoore-l4g]

Gonzalez Joseph Hernandez Anthony Martinez Brian

[@sassanmoradi1586]

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

[@FitzGeraldDuncan-p7i]

Miller Brenda Thompson Mark Lopez Sandra

[@MattieGonzalez-tw9xc]

Lee Jason Hall Robert Harris Melissa

[@Aiden_n_n]

My CPU got scared...