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isds-neu / PhyCRNet

Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
MIT License
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Can this method be applied to equations containing elements with a mixture of time and space derivatives? #19

Open 111aaabbb opened 8 months ago

111aaabbb commented 8 months ago

Hello,

Thanks a lot for this great work that you published transparently.

I have a question that can this method be applied to equations containing elements with a mixture of time and space derivatives? such like Ut+Utt+Uxxt+Uyyt=0.

And in the code, ' output = torch.cat((output[:, :, :, -1:], output, output[:, :, :, 0:2]), dim=3) ', why are the first（output[:, :, :, -2:]） and last（output[:, :, :, 0:3]） elements not symmetrical? ( why not-2: and 0:2 )

Thank you very much for your answer, looking forward to your reply！

paulpuren commented 8 months ago

Hello,

Thanks a lot for this great work that you published transparently.

I have a question that can this method be applied to equations containing elements with a mixture of time and space derivatives? such like Ut+Utt+Uxxt+Uyyt=0.

And in the code, ' output = torch.cat((output[:, :, :, -1:], output, output[:, :, :, 0:2]), dim=3) ', why are the first（output[:, :, :, -2:]） and last（output[:, :, :, 0:3]） elements not symmetrical? ( why not-2: and 0:2 )

Thank you very much for your answer, looking forward to your reply！

Hello, thanks for your interest. I think that should be fine for a mixture of time and space derivatives. Our method is applicable to the general setup of finite difference-based schemes. For example, you can do U_xx first and then compute U_xxt based on U_xx.

For the padding dimension issues, that is because the spatial dimension of the solution is 129x129, I guess.

111aaabbb commented 8 months ago

Hello, Thanks a lot for this great work that you published transparently. I have a question that can this method be applied to equations containing elements with a mixture of time and space derivatives? such like Ut+Utt+Uxxt+Uyyt=0. And in the code, ' output = torch.cat((output[:, :, :, -1:], output, output[:, :, :, 0:2]), dim=3) ', why are the first（output[:, :, :, -2:]） and last（output[:, :, :, 0:3]） elements not symmetrical? ( why not-2: and 0:2 ) Thank you very much for your answer, looking forward to your reply！

Hello, thanks for your interest. I think that should be fine for a mixture of time and space derivatives. Our method is applicable to the general setup of finite difference-based schemes. For example, you can do U_xx first and then compute U_xxt based on U_xx.

For the padding dimension issues, that is because the spatial dimension of the solution is 129x129, I guess.

Thank you very much for your answer, I will try it again!