PINNs New library
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- Опубліковано 6 вер 2024
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Physics Informed Neural Networks (PINNs)
Inverse Physics Informed Neural Networks (I-PINNs)
Simulation By Deep Neural Operator (DeepONets)
PINNs Using NVIDIA Modulus
NVIDIA Modulus: Advanced Topics
Machine Learning For Engineering : A-Z
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Just bought the course!! Going to binge-watch it this weekend. Your timing is perfect, I'm just starting my master's thesis! Thank you!
Thank you very much, Hope the course will make things easier... good luck with your masters thesis
That is really nice, I lookforward to learning more about the new library! 😀
More to come! Thank you
Thank you sir, I just bought the course! One question, Do we need a simulation / experimental data to train the model with Nvidia modulus or how is the Neural Network training (only with PDEs and boundary constraints)? Ty!
It depends on what technology you use: PINNs do not need data, data is just our ic and bc and the equations rules, some other technologies will be concerned in the future course that deals with the use of simulation or experiment generated data to model a system without the need of PDEs such as deep neural operator, or find unknown value in the governing equations using the data such as I-PINNs
I bought your lesson however we dont learn how to input our data(custom geometry for example) and how to include data driven solutions too. I think its insufficient for the time. I hope something about this is added or you can reach me out for some help. Thank you
This course is an intro for running and writing Nvidia modulus code for PINNs problems, However, we are planning to add one more chapter for the PINNs course and publish a new course to handle more advanced topics.
Now we have new chapter for stress analysis for a panel problem.