Group Abstract Group Abstract

Message Boards Message Boards

PINN: Physics Informed Neural Networks for Laplace PDE on L-shaped domain

6 Replies
Posted 1 year ago
POSTED BY: Zhang Haoyu

Hi Gianluca,

The solution to the Poisson equation \div \grad u = -1 you show does not seem to obey the boundary condition u = 0.

Compare to:

\[CapitalOmega] = 
 Region[RegionDifference[Rectangle[{-1, -1}, {1, 1}], 
   Rectangle[{0, 0}, {1, 1}]]]; Subscript[\[CapitalGamma], D] = 
  DirichletCondition[u[x, y] == 0, True];uval = NDSolveValue[{D[u[x, y], x, x] + D[u[x, y], y, y] == -1, 
   Subscript[\[CapitalGamma], D]}, 
  u, {x, y} \[Element] \[CapitalOmega]]; ContourPlot[uval[x, y], {x, y} \[Element] \[CapitalOmega]
POSTED BY: Leo Kärkkäinen

Hi Giulio, thanks for your comment.

Yes, I observed that cycling NetTrain could get some benefit for stabilizing the results, because the set of learned parameters by a previous cycle seems to be not destroyed by the next cycle. But experimenting with MaxTrainingRounds could be interesting, I have not used this feature at present.

Thanks. Gianluca

Hi Gianluca and thanks for sharing this!

One question: why do you map NetTrain instead of of using MaxTrainingRounds? Do you want to reset the learning rate?

enter image description here -- you have earned Featured Contributor Badge enter image description here Your exceptional post has been selected for our editorial column Staff Picks http://wolfr.am/StaffPicks and Your Profile is now distinguished by a Featured Contributor Badge and is displayed on the Featured Contributor Board. Thank you!

POSTED BY: EDITORIAL BOARD
Reply to this discussion
Community posts can be styled and formatted using the Markdown syntax.
Reply Preview
Attachments
Remove
or Discard