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

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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
Posted 1 year ago
POSTED BY: Zhang Haoyu

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?

POSTED BY: EDITORIAL BOARD
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