Thank you for the detailed and informative video. Easily saved me from weeks of literature survey effort by providing a review of selected papers across various ML + CFD applications. All the papers mentioned seem like great starting points to delve into the field.

Stumbled across your channel looking to brush up basics of ML in Fluids, coincidentally after reading the review article you co-authored. Super clear and easy to understand lecture! One thing I would appreciate is a references list of the papers you reference in the video in the video description. Thanks a lot for the video!

Wow, as both Mechanical Engineer(specialized in energy field) and Software Engineer, this was my dream to I want to work on :) I feel very lucky to came across to your channel.

I've just started my PhD on ML in CFD, thanks for the insight!

You are a great inspiration. It's absolute pleasure of watching your lectures and learning.

This lecture is pure gold. Having experience with both deep learning and cfd this seems somehow both achievable and straight out of a sci-fi movie

This is absolutely brilliant and inspiring. Thank you such a detailed introduction to ML for Fluid Mechanics

I’m an undergraduate major in fluid mechanics and my instructor studies both machine learning and turbulent flows. It’s good to know this topic has such good prospects

This was wonderful to help me in describing my current work challenges with ignorant executives. Beautifully done. The best by far I've ever seen. Kudo's to you!

When I first took a lesson of feedback/trained neural network a quarter century ago, I didn't think that it could incorporate existing knowledge. Thanks to this presentation, I learned that it has actually been done.

Beautiful lecture, Steve. One of my dream projects is to combine topology optimization with fluid mechanics to iterate and develop wing profiles and structures. I'll be looking up on the ways to "bake in" known data to help with the code. Thank you!

Wonderful lecture I am really interested in this integration between fluid mechanics and machine learning. I hope this series will continue

Thank You For The Brief Summary Of Field. I Enjoyed Your Overview Perspective Of The Basic Subject Matter. The Presented Information Flowed Well Towards Summation And Sufficiently Outlined The General Scope Of Functional Applications. In My Sincere Opinion, Well Done.

Thank you so much for providing such an understandable integration of the concepts. I am a Clinical Psychology Ph.D. student (with a career history as a commercial pilot). I am fascinated by the combination of fluid mechanics and psychology (the invisible biology) and what machine learning can provide to the field of psychology in the near future. The psychology field is behind... way behind, on organizing the quant and qual data into usable models--but this gives me a little hope. :)

I am surprised by the fact that this lecture is free. Really great content and clear explanation.

The modern professors like you are making things easier for new researcher like me, thank you very much for this lecture series.

An excellent method of presentation and description of the knowledge.

Thank you for this video. This helped equip me with at least a rudimentary knowledge of the topic that enabled me to ask the questions I needed to ask. You helped me get a job.

Fantastic presentation Steve. Curiosity rules! Grateful.

This is such a great video of top view of ML in FM. Loved it!

Simply amazing! Can't wait to watch more!

This is an outstanding lecture.

Thanks so much for making these videos available to the public! I'm currently going through your "Data-Driven Science and Engineering" book in group meetings at UC Berkeley, and these videos are a great additional resource

This is an excellent introduction, thank you very much, I found it tremendously helpful.

Thank you very much, such an inspiring talk and insightful overview.

Brilliant presentation, thanks for sharing!

A really wonderful Lecture. Thank you for this!

A most excellent video lecture, perfect for an introduction to fluid mechanics research.

Thank you for your wonderful lecture, Dr. Brunton. This is what I really want to learn.

thank you for presenting such an interesting topic

This looks interesting, awesome and tough at the same time. Wow!

Greeeat!! I read this year's Raissi and Karniadakis' paper in Science and I definitely want to know more

Superb. This is simply one of the best KZfaq I ever watched ! Thanks for sharing..

This was much needed thank you 😀

got this recommended after watching your Von Karman and IPAM lectures :) great stuff !

Very very nice end examples of inspiration

So nice explanation. I can't help myself subscribing your channel!

Fantastic overview, watched your video because I was getting stuck in my model I might change direction.

Thanks for posting this video

Really Enjoyed this video! Thank you

Great job as always Steve! :)

I am loving this channel

Thanks, for Integrating my two favourite fields of study. ❤❤

Thank you so much, Steve!

you are so great lector. thank you for videos

This is really interesting and worth investigating.

very inspiring lecture !

It's an outstanding lecture. I got into PhD in HPC-CFD and want to use ML,DL, ANN/CNN in CFD. Looking forward to work with you in future Sir.😁

So cool lecture, thanks👍

You are just great! Kindly share more videos on this topic

Finally some coherent talk on integration for both.

Impressive lecture, thank you very much !

Really nice lectures!!!!!!

Thank you a lot!

I love this video! I will check out the papers. Thanks

Hi, first of all I would like to thank you for that great content you are offering for free on KZfaq! I have two questions regarding any kind of reduced order models (built with AE or POD): 1) if you are decomposing the problem into time and space and you say you use "snapshots" of different moments in time, I wonder how do you get the different snapshots at different points in time WITHOUT doing the the high fidelity simulation for the whole timespan? Because in any numerical simulation I know you use one timestep after another and can't skip any timestep... And with doing the whole high fidelity simulation I wonder why would like to built a reduced order model at all? 2) Assuming one can do simulations of different snapshots: How should one space the snapshots values of the high fidelity simulations across the interval of the time/parameter space? For example if I have an intervall from 0 to 1000 and I want 10 modes, do I want modes at 0, 100, 200, ... or unequally spaced points? Is there a literature for this kind of problem? I would be very thankful for an answer and looking forward hearing from you! Kind regards, Daniel

Inspiring speech of a trending field of computational engineering.

Excellent video. Can you please make videos solving problems using ML? I'm asking for Live programming recording. This will be a great help as it will focus on "how" simulations are performed. Cheers

THIS IS AWESOME!!!

Thanks Steve

Nice talk Sir. Still, there is a lot more to be done for depicting the turbulent behavior as it's the last Unresolved Mystery of Classical Physics. It's just the beginning of a new era where we are trying to approach the same old problem but with the new tools at hand. Moreover, sir could you please recommend any book that brings together both the topics i.e AI & CFD?

Very nice video , thank you very much Steve. I just have a question the input to the deep learning model will be just pictures ??

Fantastic video!

This is EXACTLY what I was looking for...

Great work. What is your opinion about the Material Point Method(MPM) to model high-level behavior of fluid dynamics? Ppl are modeling fluid motion behavior using this method in recent papers.

Great video God help and guide you

Thanks a lot for the nice lecture, really appreciate it. Could you please let me know how can we try to solve parabolic PDEs or try to predict if the system can show the Turing patterns or not? Could you please let me know from where can I start to know how to solve PDEs in ML? I have so many questions that it is embarrassing for me to type all of them. Once again, love your lectures. Thank you.

Love to see you standing in Earthrise lmao, looked slick

Really amaaaazing Lecture! I am a newcomer in this interesting field where fluid mechanics meets ML. Any suggestions where I can get more materials in this field?

Steve, can you tell me what is the system you are using to do your presentation where it looks like you are writing on a transparent board on the other side of the board but we can see it the right way? It looks so cool.

Idea - If you define differential equations as loop functions. Ex. y[0] = ...; loop (dydt = y ; y: = y +dydt*dt) then we there should exist loop(loop(loop(...))) functions since that belongs to xyz space. Three loops to initialize a 3D volume.

I am working a atmospheric modelling. It's my pleasure to be here. I hope I will meet you in future.

This is indeed the tip of the iceberg. This topic has been discussed for the last twenty years in the field of turbulent combustion. I hope you mention them in your new review paper.

Thanks for the great video. It's very fascinating to hear neural nets constrained by physics principles. How much of background knowledge of fluid mechanics is needed to get started with this kind of problems? (if the current background is computer science and machine learning)

Top thanks for making such a Super nice video. Extremely lucky to subscribe you.

In the example on extrapolation at 23:10 we clearly see a tendency from snapshots a to b, as from the extrapolations b to c. In e) we see clearly a sudden increase in the flow field. My understanding is that this could not be predicted because the snapshots represent only a part of the flow, so unseen fluctuation affect what is visible in this limited windows (more so if these are 2d images of a 3d flow field). This makes sense?

Thankyou!

Im an ex FEA simulation engineer doing Data Science. I never thought these two fields could work together

@Steve Brunton, What would be the best book to learn machine learning for a beginner?

Thank you KZfaq for letting me find this.

Dear Steve Brunton, Thanks for the great lecture. Any suggestions on datasets to train these flow models

Steve I can't follow half of what you are saying... but I LOVE these videos!!!

Is there a structured way of approach for learning this It seems really interesting Although i am more of solid mechanics person simply becauseit is more predictable than fluid mechanics But this video makes me learn both machine learning and fluid mechanics

Wow. At about 15 minutes that noise and "corrupted pixels" in the images produces noise in the transcoding and KZfaq compression algorithm. Crazy.

Can you make complete playlist for CFD ?

Great lecture. Can someone please give insight on how industrial CFD will be changed with ML progressing in fluid mechanics. How prepared an CFD engineer should be in future?. Thank you

I would disagree that we have enough data for fluid mechanics unlike other popular problems like image recognition. Fluid problems in general are very complex and unique and as soon as one changes certain conditions, machine learning models become useless. But there is a lot of scope for control problems using reinforcement learning which as such does not solely rely on data.

Can you please make videos on non-linear control systems.

Can you suggest some accessible textbooks on the subject?

Hi Steve, I was wondering if you (or anyone who reads this) could recommend me a paper that I could replicate with an accessible database as a first try to get into this field. I have a good background in fluid mechanics but just getting started in machine learning, so any comment would be appreciated! Great content!

Thank you.

Thank you Steve, if you could please elaborate on this and show us some practical examples/applications that we can do using python/matlab it would be great.

Thanks this is a very inspiring video. Could ML for Fluid Mechanics be used to train neonatal artificial respiration systems for best parameterization of air flow intensity with respect to the very small sensible patients lungs - to avoid too strong air flow caused by inaccurate manually parametrization ? I learned that manually parametrizion seems to quit difficult with the risk to be not exactly enough so that early born babies lungs have much higher risk to be negative effected by artificial respiration.

Great....From where should I learn ML for FM?

Thank you steve. I was wondering if a control engineer has to know machine learning not to fall behind.

Is there a way to implement pytorch in OpenFOAM? It would be cool if there is a way to use the output of every step in OpenFOAM sn input for neural network so that it gets better with every simulation. And when it is finally trained you can use the neural network as a solver in OpenFOAM again... :-)

Can anyone please suggest to me a book to read or a course to enroll in to develop a skill in machine learning-aided cfd?

Can machine learning can used for solid mechanics... Please make a video in this topic also

God knows how many times I've seen this video.

Which are good universities for studying "Machine Learning for Fluid Mechanics"? Can anyone explain?