I already love this series! I honestly think that the choice of the problem to model is BY FAR the most important one. You can bake so much prior knowledge into that alone, it can totally make or break the entire endevour.
As a Chemical Engineer that studied CFD in grad school turned Data Scientist, I absolutely love this and the fact that there is active research in the intersection of physics and AI.
Would have really appreciated some concrete examples and case studies. With concrete math and code. I loved watching many of your videos from the databook series, because they were so unique- using math and code. And you are, always, a superb teacher, explainer. Thank you for making this series. There's really a lack of good content in this area. I really am grateful, and appreciate you doing this. Will wait for future videos. 😇😊
Thank you for posting this knowledge. I've been watching almost exclusively your content over the last year in 2023. I found super interesting the case studies you shared about the super alloy at Rolls-Royce and the predictive shimming at Boeing. It would be amazing if we could see more case studies like that. I am trying to wrap my head around on how to approach a ML model that will predict perceived color of different materials taking as input data about various processes of production for the respective materials.
Computer just give you a answer because we just program it to answer. We still don't know enough about our brain process to make the computer stimulate our brain curious process, and the way we control that curious not gone wrong, we still don't research enough to make physical part(hardware) to stimulate structure to run that function
I never really undertood the need for the term multiphysics. There are certainly different length and time scales in complex phenomena like cloud formation, but those processes are, as far as I am aware of, governed by physics (not multiphysics). Do we also apply the same idea to math, when we refer to different fields of mathematics when solving a problem? Something like multimathematics?
I get the impression that it basically means multi-scale-physics. If people find the term “multiphysics” more convenient than “multi-scale-physics” (or “a combination of physical models that model physics of things at different scales”), I don’t have a problem with that.
Multiphysics is just to highlight the need for multiple domain knowledge under the umbrella of "physics". For example, you can call transport phenomena and electromagnetic field theory are just "physics", as opposed to chemistry, biology, right? But you can also say they are different physics --- physical mechanisms.
So what would be the 'hello world!' tutorial/dataset of Physics Informed machine learning? Some general 'hello world!'s in machine learning are MNIST(handwriting Digits identification), Iris Flowers Classification, or Cancer , Ham v Spam (email), etc. The first two are notable as to how relatable they are that one could imagine making the dataset themselves, though really with a lower sample size due to the effort involved vs a real dataset.
So as for physics EMBEDDED machine learning (I am sticking with 'embedded' as opposed to 'informed' because it's closer to the design and 'informed' sounds a bit old-academic and is less 'tactile' to visualization and interpretation - which is very important). But it could be 'data science' or 'cryptographic' embedded machine learning right? And that's what we could be seeing demonstrated by algorithms like Q-star (differentiating between encrypted and plain text data-sets to crack encryption standards). I believe Sora is using Unreal Engine 5 for its training data (synthetic), and the power in the physics is evident when you have potentially infinite choice and combination of physical scenario, as syntetic data allows....accelerating numerical computations by taking a simulation at low-res then scaling up in resolution by way of augmented machine learning is simply MASSIVE! - just in-terms of the sheer affect on research and industry, chip design and manufacture (I would have kept silent with regards the cov. vaccine incidentally, and we won't have the long term vaccine-injury data on that for about another 20 years or so...that's a HOWLER I'm afraid 😞
Anybody knows how to setup the recording room this way? (Looks like an acrylic screen, where he usually writes by hand, but now he's sort of using is a projectint surface, is this in post?, anyway) Great content!
The teacher (Yoda) is dual to the pupil (Luke Skywalker) -- The Hegelian dialectic. Master (Lordship, client) is dual to the slave (bondsman, server) -- The Hegelian dialectic. Problem, reaction, solution -- The Hegelian dialectic. "Always two there are" -- Yoda.
Hi I student want to use artificial intelligence in aerospace aerodynamic can you show me the step by step how to start and wich book should read (the point start and to the end point)??? If you explain in the a clip is great
Too much blah blah. Would be more useful if we'd actually start solving problems with code and math. All this talk just comes in one eat and goes out the other without practice.
No, it is going in one ear and out of the other because you’re not taking notes like a good student who knows how to learn something from a lecture. It’s also part of a series; here he’s covering a first stage of problem solving that comes before coding. I suggest you resist the immature impulse to code before having done any intellectual work.
Coding without understanding is just wasting your time. If you can't understand what he is saying then I'd try another subject. He is a really very good teacher
![AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning]](http://i.ytimg.com/vi/g-S0m2zcKUg/mqdefault.jpg)
![AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning]](/img/tr.png)
![AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning]](http://i.ytimg.com/vi/GCz6afDVy5Y/mqdefault.jpg)
Thanks for posting these lessons. There isn’t enough good material about this out there.
Hi professor brunton. thank you for this lectures. i am really enjoying your videos. can't wait for the next one.
This course is one of the best learning tool on the internet. Thank you Mr Brunton
I already love this series! I honestly think that the choice of the problem to model is BY FAR the most important one. You can bake so much prior knowledge into that alone, it can totally make or break the entire endevour.
Thanks for sharing your knowledge with us all! I feel fortunate to be able to access this level of learning for free
As a Chemical Engineer that studied CFD in grad school turned Data Scientist, I absolutely love this and the fact that there is active research in the intersection of physics and AI.
same as mechanical engineer work 3 year cfd engineer currently working on the ai robotics engineer
Would have really appreciated some concrete examples and case studies. With concrete math and code.
I loved watching many of your videos from the databook series, because they were so unique- using math and code. And you are, always, a superb teacher, explainer.
Thank you for making this series. There's really a lack of good content in this area. I really am grateful, and appreciate you doing this.
Will wait for future videos. 😇😊
42:05 "... you don't want to be in the crystal energy group..."
Ah, those pesky condensed matter physicists!
Very interesting, can't wait to see where you take this!
Super stoked to see our car in the presentation 😊
Awesome lecture. God bless you for sharing this knowledge on youtube.
This is excellent - cant wait to see the whole series
Quality content is an understatement Waiting for more
Thank you for posting this knowledge. I've been watching almost exclusively your content over the last year in 2023. I found super interesting the case studies you shared about the super alloy at Rolls-Royce and the predictive shimming at Boeing. It would be amazing if we could see more case studies like that. I am trying to wrap my head around on how to approach a ML model that will predict perceived color of different materials taking as input data about various processes of production for the respective materials.
Beautiful, just beautiful...Thank you
One suggestion: on shape engineering, MIT made the toroidal propeller. Maybe do a case study on that? Like walk us through the process
Thank you for the lectures, learned/got inspired a lot.
Thank you for doing these excellent lectures Dr Brunton
Computer just give you a answer because we just program it to answer. We still don't know enough about our brain process to make the computer stimulate our brain curious process, and the way we control that curious not gone wrong, we still don't research enough to make physical part(hardware) to stimulate structure to run that function
Thank you for this excellent lecture. Learned a lot.
Really amazed, Thank you Prof. Brunton.
Amazing! Thank you so much for this set of lectures!
Excellent lecture! Many thanks professor!!!
Great tutorial 😊 thanks so much
You are a legend, professor.
Thanks!
Great thanks!
I never really undertood the need for the term multiphysics. There are certainly different length and time scales in complex phenomena like cloud formation, but those processes are, as far as I am aware of, governed by physics (not multiphysics). Do we also apply the same idea to math, when we refer to different fields of mathematics when solving a problem? Something like multimathematics?
I get the impression that it basically means multi-scale-physics.
If people find the term “multiphysics” more convenient than “multi-scale-physics” (or “a combination of physical models that model physics of things at different scales”), I don’t have a problem with that.
Multiphysics is just to highlight the need for multiple domain knowledge under the umbrella of "physics". For example, you can call transport phenomena and electromagnetic field theory are just "physics", as opposed to chemistry, biology, right?
But you can also say they are different physics --- physical mechanisms.
I really appreciated you for your efforts.
I see Steve+AI, I click, I like
So what would be the 'hello world!' tutorial/dataset of Physics Informed machine learning?
Some general 'hello world!'s in machine learning are MNIST(handwriting Digits identification), Iris Flowers Classification, or Cancer , Ham v Spam (email), etc.
The first two are notable as to how relatable they are that one could imagine making the dataset themselves, though really with a lower sample size due to the effort involved vs a real dataset.
There is a step zero. 0. Watch and thoroughly absorb this video.
@stevebrunton can you share a git repo with a basic project with problem description , setup env , ML model
Great professor, Thank you. can you please provide"HANDS-ON" lessions on python.
So as for physics EMBEDDED machine learning (I am sticking with 'embedded' as opposed to 'informed' because it's closer to the design and 'informed' sounds a bit old-academic and is less 'tactile' to visualization and interpretation - which is very important). But it could be 'data science' or 'cryptographic' embedded machine learning right? And that's what we could be seeing demonstrated by algorithms like Q-star (differentiating between encrypted and plain text data-sets to crack encryption standards). I believe Sora is using Unreal Engine 5 for its training data (synthetic), and the power in the physics is evident when you have potentially infinite choice and combination of physical scenario, as syntetic data allows....accelerating numerical computations by taking a simulation at low-res then scaling up in resolution by way of augmented machine learning is simply MASSIVE! - just in-terms of the sheer affect on research and industry, chip design and manufacture (I would have kept silent with regards the cov. vaccine incidentally, and we won't have the long term vaccine-injury data on that for about another 20 years or so...that's a HOWLER I'm afraid 😞
Im excited, are there groups/communities for the general public to join for this topic ?
Thx for core steps
Where can I find the text book ?
And thnx your explanation is what help me to really understand what I missed
YES! THANK YOU BOSS!
Ok is the video tuned with ML to my research?? I’m literally working on discovering new physics for plasmas in spherical tokamaks! Spooky…
Anybody knows how to setup the recording room this way? (Looks like an acrylic screen, where he usually writes by hand, but now he's sort of using is a projectint surface, is this in post?, anyway)
Great content!
I really can't thank you enough.
What happened to the 3rd lecture on architectures?
Saludos desde Colombia.
This is amazing!
The teacher (Yoda) is dual to the pupil (Luke Skywalker) -- The Hegelian dialectic.
Master (Lordship, client) is dual to the slave (bondsman, server) -- The Hegelian dialectic.
Problem, reaction, solution -- The Hegelian dialectic.
"Always two there are" -- Yoda.
The second video is missing, where can I find it?
Hi I student want to use artificial intelligence in aerospace aerodynamic can you show me the step by step how to start and wich book should read (the point start and to the end point)???
If you explain in the a clip is great
You would think all ML models are "physics-informed" to function correctly...heck, even just to work (to be able to run).
You never put the links you mention.
where is the math?
why is the math?
How is the math?
Who is the math?
What the fuck is math?
Too bad you used Alpine as the example car....
25:00
Thanks for these lectures but you could be more succinct.
I like the notions he has on astrology and ai powered products advertisement. 😂 . Please don't take a week to upload chapters. Upload all at once.
🦜🦜🦜🦜
Too much blah blah. Would be more useful if we'd actually start solving problems with code and math. All this talk just comes in one eat and goes out the other without practice.
No, it is going in one ear and out of the other because you’re not taking notes like a good student who knows how to learn something from a lecture.
It’s also part of a series; here he’s covering a first stage of problem solving that comes before coding.
I suggest you resist the immature impulse to code before having done any intellectual work.
Can you elaborate on "just start solving problems with code and maths"
Coding without understanding is just wasting your time. If you can't understand what he is saying then I'd try another subject. He is a really very good teacher
If only he had made a video explaining the importance of understanding your problem before jumping into the math and coding...
This is not blah blah...this is the motivation to start the topic.