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View Code? Open in Web Editor NEWPyTorch Implementation of Physics-informed Neural Networks
PyTorch Implementation of Physics-informed Neural Networks
class Net:
...
def loss_func(self):
...
loss_pde = self.criterion(du_dt + u.squeeze() * du_dx, 0.01 / math.pi * du_dxx)
...
return loss
Here the code of loss_pde = self.criterion(du_dt + u.squeeze() * du_dx, 0.01 / math.pi * du_dxx)
of coefficient 0.01 / math.pi need to be (0.01 / math.pi) , and the code will be loss_pde = self.criterion(du_dt + u.squeeze() * du_dx, (0.01 / math.pi) * du_dxx)
.
Then the result will change. It's so amazing.
Hello, thanks a lot for your hard work and your sharing.
I would like to ask why two optimizers are needed during the training.
self.optimizer.step(self.loss_func)
self.optimizer_Adam.step()
Thank you so much in advance for your answers.
when i run file "Burgers Inference (PyTorch).ipynb" error_u = np.linalg.norm(u_star-u_pred,2)/np.linalg.norm(u_star,2)
in the folder “continuous_time_inference (Burgers)”, An error occurred that
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
The L2 test error in the original PINN text is 6.7e-4, but training with the pytorch version yields results of 1e-3~3e-3. What is the reason for this?
In the function 'loss_func' of 'Net class' of"Burgers.ipynb", do we need to add 'self.adam.zero_grad()' ?
Hi @jayroxis and thank you very much for this useful repository, really appreciated.
I was wondering if by any chance your pytorch implementation for the Burgers Equation inference solution, "time discrete" approach was also available and open to public?
Thank you again!
PS: sorry for asking this in an issue, I could not find your email.
Hey Jay,
Thanks so much for creating this repository. It is quite helpful. I was wondering how you created the "burgers_shock.mat" data? Is this data computed by analytically solving the Burgers equation? Would it be possible to have the code you used to generate that?
Best,
-Amir
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