Відео

Fantastic series of lectures.

great video ! such cool research area

This was a really fantastic lecture.

I got one question about curse of dimensionality in PINN. The last example you showed you sampled a lot of data to train the network. isn‘t that cursive dimensionality? If you only care about 1D case, do we need to sample of that amount of data? Thanks for the lecture

I got one question about a curse of dimensionality. The last example your showed you sampled a lot of data to train the network. isn‘t that cursive dimensionality? If you only care about 1D case, do we need to sample of that amount of data? Thanks for the lecture

Why is regression supervised?

are there videos for the tutorial

Is the notebook file available on the github?

I have a question about classification, what happens when the y is a multiclass classification problem

This is the most important and valuable playlist on my youtube right now. thanks for sharing

thank you so much for this lecture,

what is diag(sigma'(g))?

insightful

Thank you for putting these lectures online. They have been an invaluable resource for my study. Ben is a great teacher, I love how he focuses on establishing understanding of the concepts, links and implications rather than getting bogged down in mathematical details. And taking a moment to pause to make sure everyone understands key concepts. The questions to the class are great too. I like to pause the video and think about an answer. If I can't, often he'll say it's a non-obvious one which makes me feel much better! 😅Thanks again 👏

I wonder if PINNs can solve the three-bodies problem :D

awesome lecture. Since I am new to PINNs, I just want to know what if we could not have access to the PDEs formulation.....

thank you for sharing sientific ml knowledge !

How can I register in this course in place. I am not a student at ETH

related to what was talked about @5:37, are there any cases where you can not find true function no matter what you do(architecture, density , number of parameters, etc), you cannot reach that point in the function space?

Id be happy to sponsor a microphone for a better audio experience

Hello. Can you please provide the ppts of these lectures?

Excellent PINN classes with real application examples!!

Thanks a lot for sharing. I wish you best of luck for all

Pretty bad lecture

Hi, Fantasctic lessons ! Any chance to get the slides ?? Thanks !

This is amazing!

it's great, hardly find teach code video of PINN

Thank you for the video. Could you clarify please why ReLU is not used as an activation function?

Is the Jupyter file of the harmonic oscillator demo available anywhere?

Great lectures! I believe this is best resource out there for an enthusiast to get started with Scientific ML. I was wondering if it would be possible to share the lecture slides by any chance?

I was searching for some good resources to learn PINNs and came across these video lectures. Thanks for sharing these; the course content and the way of teaching, I really liked it and learnt a lot from this course. Thank you !!

So good. I am literally binge-watching ! Haha!

Very helpful, thank you!

Can the slides be made available for private use ? Thanks

Lecturer's way of teaching is very odd, a bit rude if I may say. Thanks for the course.

Great overview of PINN in 2023

Thank you for the great lecture. Question: I understand how periodic boundary conditions can work with discretized PINNs, but how are they supposed to fork with fully connected networks (continuous PINNs that use Autograd)?

why is the physics loss is 0?

Awesome 👌. Thanks for sharing valuable knowledge about the topic.

Can we get the lecture slides, it would be helpful.

There is only one like which you may not care about? please share more!

The Tutorials are not organized properly in the github repo. Kindly help with that

Would it be possible to add the order of the tutorials in the Readme file? Or maybe to rename them with the order number? Thank you

Thanks for sharing!

Many thanks for uploading such a course from those who can not reach such a high quanlity of education🤍

We could really benefit from the tutorials where there is code and implementation. Most videos are the professor writing some Math symbols on screen. I must say that these videos are pretty boring. And it is like a recorded book and not a proper lecture. From the next iterations of this course, please record the tutorials. I would rather watch those than this spoken math textbook.

what a great topic, and such nice lectures! gained much clarity!

Wonderful course. Can we please have the slides for each lecture?

Why there is no collocation loss term in the second example?

great course