Replies: 1 comment
-
|
DeepXDE is very easy to modify. |
Beta Was this translation helpful? Give feedback.
0 replies
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Uh oh!
There was an error while loading. Please reload this page.
-
Hi all,
I am researching the effects of normalization for layer outputs in neural networks, similar to this paper: https://arxiv.org/pdf/2209.01018.pdf, but applying it to PDEs.
I have been using my custom architecture, but I've run into problems, which would be an easy fix if I was using DeepXDE (namely switching from one optimizer to another).
My question is: do you think it'd be easier to modify the source code to achieve the scaling, or should I stick to my custom implementation, and try to implement the switching?
Beta Was this translation helpful? Give feedback.
All reactions