ee39b39805
* 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>
73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
""" Run PINA on ODE equation. """
|
|
import argparse
|
|
import torch
|
|
from torch.nn import Softplus
|
|
|
|
from pina import LabelTensor
|
|
from pina.model import FeedForward
|
|
from pina.solvers import PINN
|
|
from pina.plotter import Plotter
|
|
from pina.trainer import Trainer
|
|
from problems.poisson import Poisson
|
|
|
|
|
|
class myFeature(torch.nn.Module):
|
|
"""
|
|
Feature: sin(x)
|
|
"""
|
|
|
|
def __init__(self):
|
|
super(myFeature, self).__init__()
|
|
|
|
def forward(self, x):
|
|
t = (torch.sin(x.extract(['x'])*torch.pi) *
|
|
torch.sin(x.extract(['y'])*torch.pi))
|
|
return LabelTensor(t, ['sin(x)sin(y)'])
|
|
|
|
if __name__ == "__main__":
|
|
|
|
parser = argparse.ArgumentParser(description="Run PINA")
|
|
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 []
|
|
args = parser.parse_args()
|
|
|
|
# create problem and discretise domain
|
|
problem = Poisson()
|
|
problem.discretise_domain(n=20, mode='grid', locations=['D'])
|
|
problem.discretise_domain(n=100, mode='random', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
|
|
|
|
# create model
|
|
model = FeedForward(
|
|
layers=[10, 10],
|
|
output_dimensions=len(problem.output_variables),
|
|
input_dimensions=len(problem.input_variables) + len(feat),
|
|
func=Softplus
|
|
)
|
|
|
|
# create solver
|
|
pinn = PINN(
|
|
problem=problem,
|
|
model=model,
|
|
extra_features=feat,
|
|
optimizer_kwargs={'lr' : 0.001}
|
|
)
|
|
|
|
# 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)
|
|
|
|
|
|
if args.load:
|
|
pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=problem, model=model, extra_features=feat)
|
|
plotter = Plotter()
|
|
plotter.plot(pinn)
|
|
else:
|
|
trainer.train() |