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Fix #316 (#324)

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* solving issue
This commit is contained in:
Dario Coscia
2024-08-12 14:47:14 +02:00
committed by GitHub
parent d4ced3a7d7
commit d00fb95d6e
2 changed files with 23 additions and 27 deletions
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29
examples/run_parametric_elliptic_optimal.py
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@@ -5,7 +5,7 @@ from torch.nn import Softplus
from pina import LabelTensor
from pina.solvers import PINN
from pina.model import MultiFeedForward
from pina.model import MultiFeedForward, FeedForward
from pina.plotter import Plotter
from pina.trainer import Trainer
from problems.parametric_elliptic_optimal_control import (
@@ -25,18 +25,17 @@ class myFeature(torch.nn.Module):
return LabelTensor(t, ['k0'])
class CustomMultiDFF(MultiFeedForward):
class PIArch(MultiFeedForward):
def __init__(self, dff_dict):
super().__init__(dff_dict)
def forward(self, x):
out = self.uu(x)
out = self.uy(x)
out.labels = ['u', 'y']
p = LabelTensor(
(out.extract(['u']) * x.extract(['alpha'])), ['p'])
return out.append(p)
z = LabelTensor(
(out.extract(['u']) * x.extract(['alpha'])), ['z'])
return out.append(z)
if __name__ == "__main__":
@@ -55,15 +54,15 @@ if __name__ == "__main__":
# create problem and discretise domain
opc = ParametricEllipticOptimalControl()
opc.discretise_domain(n= 100, mode='random', variables=['x1', 'x2'], locations=['D'])
opc.discretise_domain(n= 5, mode='random', variables=['mu', 'alpha'], locations=['D'])
opc.discretise_domain(n= 20, mode='random', variables=['x1', 'x2'], locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
opc.discretise_domain(n= 5, mode='random', variables=['mu', 'alpha'], locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
opc.discretise_domain(n= 900, mode='random', variables=['x1', 'x2'], locations=['D'])
opc.discretise_domain(n= 5, mode='random', variables=['mu', 'alpha'], locations=['D'])
opc.discretise_domain(n= 200, mode='random', variables=['x1', 'x2'], locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
opc.discretise_domain(n= 5, mode='random', variables=['mu', 'alpha'], locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
# create model
model = CustomMultiDFF(
model = PIArch(
{
'uu': {
'uy': {
'input_dimensions': 4 + len(feat),
'output_dimensions': 2,
'layers': [40, 40, 20],
@@ -83,6 +82,8 @@ if __name__ == "__main__":
if args.load:
pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=opc, model=model, extra_features=feat)
plotter = Plotter()
plotter.plot(pinn, fixed_variables={'mu' : 1 , 'alpha' : 0.001}, components='y')
plotter.plot(pinn, fixed_variables={'mu' : 3 , 'alpha' : 1}, components='u')
plotter.plot(pinn, fixed_variables={'mu' : 3 , 'alpha' : 1}, components='z')
plotter.plot(pinn, fixed_variables={'mu' : 3 , 'alpha' : 1}, components='y')
else:
trainer.train()