Application 4 - Solution of PDE/ODE using Neural Networks

Application 4 - Solution of PDE/ODE using Neural Networks

[ajithjoseph]

Finally found the best explanation for PINN. Thank you for making it simple which helped me to understand the concept.

[normalperson]

Great video. Amazing content available for free by NPTEL by one of the finest teachers.

[DanielSchaefer]

Really great video explaining the concept behind PINNs!

[zaiyuanlu]

Absolutely amazing, I'm watching it so smoothly as if I'm watchin a movie

[n.w.]

I rarely comment on youtube videos but, I gotta say, this is really good and very well explained content!

[morzariadeep]

I've been working in CFD for quite some time and I must tell you that this is a game changer !! amazing stuff

[adarshkumargupta]

Highly Dont move around, this is Master of others.

[amartyasv]

Excellent video. Did a good job of explaining the essence of the paper and topic in general

[artificialintelligence]

Thank you Sir for explaining such a difficult topic deligently

[florianp]

This explanation was extremely clear and useful! Thank you sir!

[yantzanzh]

This video is really useful. All explanations are so clear. Thank you. BTW, would you mind giving a short video about the inverse problems using this method? Thank you.

[hamdiamroun]

very well explained but unfortunately the big missing here is the access to a concrete example code and allow to the audience to play with it

[nevokrien]

Thinking about it before the solution is told.

The way u solve this is by calculating an aproximation for the derivatives pluging that to the pde the absolute value of what should be zero is the error

[rahulgundawar]

I must say, it's great I have a question, after training it, how can we predict the results for different inputs??

[johnjaqui]

Where could I find the other lectures?

[TheGameDuke]

Amazing video! I have one question, the results from the NN, what is compare to in the loss function in order to calculate the difference?

[vuppumadhuri]

Dear Sir, Can you help me understand how to use this algorithm for second-order ODEs? I understood the theoretical content in this video very well but I am at a loss on understanding how to specify the cost function in the program. If the second-order ODE 𝑑2
𝑥𝑚 (𝑡)/𝑑𝑡2 = −2*𝑘𝑚*𝑏𝑚*𝑑𝑥𝑚 (𝑡)/𝑑𝑡 − 𝑘𝑚2𝑥𝑚 (𝑡)+ 𝐺𝑚*𝑘𝑚 (𝑝𝑚 +C*𝑆 (𝑥𝑛 (𝑡))) is in this way and how can we specify this in a cost function? Can you kindly provide an example code to understand better? I am learning how to solve ODEs through neural networks. Thank you very much for providing us wonderful content.

[joshuamills]

Thanks for the fantastically clear explanation. I have a question, does this novel idea allow for more efficient solving than current solvers??

[aloksrivastava]

there are few problem in taking cost function 1. boundary conditions are not eniterly meet unlike FEM. 2. if we take some multiplier to increase satisfaction of boundary condition we compromise on differential equation

[mingx]

Very great video. Thank you very much Sir. Could you direct me to the list with the full videos of this course. Tried to look for it under NPTEL-NOC IITM channel, but just way too many courses and videos, do not know where the other videos are located.