Machine studying is quickly turning into a core expertise for scientific computing, with quite a few alternatives to advance the sphere …
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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
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.
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.
Excellent video! Really enjoyed the comments in the final about reporting the time to train the model and being aware that there is a learning curve and that we "can't expect these to immediately beat the state-of-the-art".
Music cool! Name, please?
So much computing for almost no relevant result…
insane work
Bravo Steve !!
One video on turbulence model with fourier transform
What is the physical meaning of each POD
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.
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