svkarash
svkarash
Dear @praksharma, you are right. However, those are the same as I read manual and command description. Assume that the command is psi_xx = dde.grad.hessian(psi, x). Since I was working...
> Hey I think I found the issue. According to the definition of Jacobian and Hessian [here](https://github.com/lululxvi/deepxde/blob/e030a3d8d3503bfca006c505271cefa827833ec5/deepxde/gradients.py#L6), you need to use I,j to decide which column of x and y...
Dear @praksharma , Thanks for clear explanation. Consider X=[x,z,t] and U=[psi,eta] Therefore, psi_xx = dde.grad.hessian(U, X, component=0, i=0, j=0) Now to calculate higher order derivatives such as psi_xxxx or psi_xxzz...
> What happens when you decrease your domain size and/or increase the amount of training points? Thanks for your reply. In both scenarios, the estimation of eta leading to unexpected...
> It may also be worth looking into using Anchors to specify specific points in your domain and/or on your boundary so that the PINN is forced to learn the...
> 1. Try to run for more iterations and more points. If your loss is Dear Reza [@AJAXJR24] Thanks for your suggestions. I'm planning to implement your suggestions. I think...
> anchors = anchors Dear @jdellag, Thank you so much for sharing the link and code. I am very excited to read and apply it. hope it help me
> Dear @svkarash , > > I already tried to solve almost the same problem. Based on the help of @forxltk , **may be** you should define the free surface...
@Nimava, I'd like to connect with you to discuss certain aspects of the problem and learn more from you. How can I get in touch with you?