@zongyi-li
Hello. Hopefully, everyone is doing well.
I have already developed an FNO-based model for a 2D (xy) Darcy flow problem using generated samples. There is potential to further improve the FNO’s performance by including physics constraints in its network configuration. That is why I am going to apply PINO for my case study.
It seems that the train_2d (line 78) part of the below is beneficial:
https://github.com/neural-operator/PINO/blob/master/train_operator.py
I will be highly appreciative if one can help me to clear the points below:
As I have my data, there is no need for lines 86-91 anymore. Am I right? Meantime, I do not understand what ‘sub’ and ‘pde_sub’ are.
There is nothing about ‘pad_ratio’ in ‘config’.
In line 99, what does ‘checkpoint’ refer to?
More importantly, there is nothing about including PDE and initial/boundary conditions. I need a comprehensive explanation of how to do so.
@zongyi-li Hello. Hopefully, everyone is doing well. I have already developed an FNO-based model for a 2D (xy) Darcy flow problem using generated samples. There is potential to further improve the FNO’s performance by including physics constraints in its network configuration. That is why I am going to apply PINO for my case study. It seems that the train_2d (line 78) part of the below is beneficial: https://github.com/neural-operator/PINO/blob/master/train_operator.py I will be highly appreciative if one can help me to clear the points below: As I have my data, there is no need for lines 86-91 anymore. Am I right? Meantime, I do not understand what ‘sub’ and ‘pde_sub’ are. There is nothing about ‘pad_ratio’ in ‘config’. In line 99, what does ‘checkpoint’ refer to? More importantly, there is nothing about including PDE and initial/boundary conditions. I need a comprehensive explanation of how to do so.