Machine Learning for Computational Fluid Dynamics

Machine learning is rapidly becoming a core technology for scientific computing, with numerous opportunities to advance the field of computational fluid dynamics. This paper highlights some of the areas of highest potential impact, including to accelerate direct numerical simulations, to improve turbulence closure modeling, and to develop enhanced reduced-order models. In each of these areas, it is possible to improve machine learning capabilities by incorporating physics into the process, and in turn, to improve the simulation of fluids to uncover new physical understanding. Despite the promise of machine learning described here, we also note that classical methods are often more efficient for many tasks. We also emphasize that in order to harness the full potential of machine learning to improve computational fluid dynamics, it is essential for the community to continue to establish benchmark systems and best practices for open-source software, data sharing, and reproducible research.

The Potential of Machine Learning to Enhance Computational Fluid Dynamics
Ricardo Vinuesa, Steven L. Brunton

This video was produced at the University of Washington

[hasnaouiacademy]

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

[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

[vptssbn]

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.

[AD-oxng]

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!

[cambridgebreaths]

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

[kingsleyzissou]

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.

[VirturaD]

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!

[apocalypt]

Amazing video. Thank you so much both of you

[HamidReza-vloj]

As always very nice and inspiring lecture.

[ruyaz]

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

[iheavense]

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

[liuyq]

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

[jti]

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

[withawintvil]

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

[__--JY-Moe--__]

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!

[utente]

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.

[yuchenma]

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

[AMADEOSAM]

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

[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?