folivo/PINA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Examples update for v0.1 (#206)

Browse Source 
* modify examples/problems
* modify tutorials

---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-235.eduroam.sissa.it>
Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
This commit is contained in:
Dario Coscia
2023-11-14 18:24:07 +01:00
committed by Nicola Demo
parent 0d38de5afe
commit ee39b39805
19 changed files with 605 additions and 613 deletions
Download Patch File Download Diff File Expand all files Collapse all files

62
examples/run_parametric_poisson.py
View File

@@ -1,7 +1,8 @@
import argparse
import torch
from torch.nn import Softplus
from pina import Plotter, LabelTensor, PINN
from pina import Plotter, LabelTensor, Trainer
from pina.solvers import PINN
from pina.model import FeedForward
from problems.parametric_poisson import ParametricPoisson
@@ -25,41 +26,48 @@ class myFeature(torch.nn.Module):
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run PINA")
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("-s", "-save", action="store_true")
group.add_argument("-l", "-load", action="store_true")
parser.add_argument("id_run", help="number of run", type=int)
parser.add_argument("features", help="extra features", type=int)
parser.add_argument("--load", help="directory to save or load file", type=str)
parser.add_argument("--features", help="extra features", type=int)
parser.add_argument("--epochs", help="extra features", type=int, default=1000)
args = parser.parse_args()
if args.features is None:
args.features = 0
# extra features
feat = [myFeature()] if args.features else []
poisson_problem = ParametricPoisson()
# create problem and discretise domain
ppoisson_problem = ParametricPoisson()
ppoisson_problem.discretise_domain(n=100, mode='random', variables = ['x', 'y'], locations=['D'])
ppoisson_problem.discretise_domain(n=100, mode='random', variables = ['mu1', 'mu2'], locations=['D'])
ppoisson_problem.discretise_domain(n=20, mode='random', variables = ['x', 'y'], locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
ppoisson_problem.discretise_domain(n=5, mode='random', variables = ['mu1', 'mu2'], locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
# create model
model = FeedForward(
layers=[10, 10, 10],
output_variables=poisson_problem.output_variables,
input_variables=poisson_problem.input_variables,
func=Softplus,
extra_features=feat
output_dimensions=len(ppoisson_problem.output_variables),
input_dimensions=len(ppoisson_problem.input_variables) + len(feat),
func=Softplus
)
pinn = PINN(poisson_problem, model, lr=0.006, regularizer=1e-6)
# create solver
pinn = PINN(
problem=ppoisson_problem,
model=model,
extra_features=feat,
optimizer_kwargs={'lr' : 0.006}
)
if args.s:
# create trainer
directory = 'pina.parametric_poisson_extrafeats_{}'.format(bool(args.features))
trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=args.epochs, default_root_dir=directory)
pinn.span_pts(
{'variables': ['x', 'y'], 'mode': 'random', 'n': 100},
{'variables': ['mu1', 'mu2'], 'mode': 'grid', 'n': 5},
locations=['D'])
pinn.span_pts(
{'variables': ['x', 'y'], 'mode': 'grid', 'n': 20},
{'variables': ['mu1', 'mu2'], 'mode': 'grid', 'n': 5},
locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
pinn.train(10000, 100)
pinn.save_state('pina.poisson_param')
else:
pinn.load_state('pina.poisson_param')
if args.load:
pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=ppoisson_problem, model=model, extra_features=feat)
plotter = Plotter()
plotter.plot(pinn, fixed_variables={'mu1': 0, 'mu2': 1}, levels=21)
plotter.plot(pinn, fixed_variables={'mu1': 1, 'mu2': -1}, levels=21)
plotter.plot(pinn, fixed_variables={'mu1': 1, 'mu2': -1})
else:
trainer.train()