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

49
examples/run_stokes.py
View File

@@ -1,55 +1,52 @@
import argparse
import numpy as np
import torch
from torch.nn import ReLU, Tanh, Softplus
from torch.nn import Softplus
from pina import PINN, Plotter
from pina import Plotter, Trainer
from pina.model import FeedForward
from pina.adaptive_functions import AdaptiveSin, AdaptiveCos, AdaptiveTanh
from pina.solvers import PINN
from problems.stokes import Stokes
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 = argparse.ArgumentParser(description="Run PINA")
parser.add_argument("--load", help="directory to save or load file", type=str)
parser.add_argument("--epochs", help="extra features", type=int, default=1000)
args = parser.parse_args()
# create problem and discretise domain
stokes_problem = Stokes()
stokes_problem.discretise_domain(n=1000, locations=['gamma_top', 'gamma_bot', 'gamma_in', 'gamma_out'])
stokes_problem.discretise_domain(n=2000, locations=['D'])
# make the model
model = FeedForward(
layers=[10, 10, 10, 10],
output_variables=stokes_problem.output_variables,
input_variables=stokes_problem.input_variables,
output_dimensions=len(stokes_problem.output_variables),
input_dimensions=len(stokes_problem.input_variables),
func=Softplus,
)
# make the pinn
pinn = PINN(
stokes_problem,
model,
lr=0.006,
error_norm='mse',
regularizer=1e-8)
optimizer_kwargs={'lr' : 0.001}
)
if args.s:
# create trainer
directory = 'pina.navier_stokes'
trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=args.epochs, default_root_dir=directory)
pinn.span_pts(200, 'grid', locations=['gamma_top', 'gamma_bot', 'gamma_in', 'gamma_out'])
# pinn.span_pts(2000, 'random', locations=['D'])
pinn.span_pts(2000, 'random', locations=['D1'])
pinn.span_pts(2000, 'random', locations=['D2'])
pinn.train(10000, 100)
with open('stokes_history_{}.txt'.format(args.id_run), 'w') as file_:
for i, losses in pinn.history_loss.items():
file_.write('{} {}\n'.format(i, sum(losses)))
pinn.save_state('pina.stokes')
else:
pinn.load_state('pina.stokes')
if args.load:
pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=stokes_problem, model=model)
plotter = Plotter()
plotter.plot(pinn, components='ux')
plotter.plot(pinn, components='uy')
plotter.plot(pinn, components='p')
else:
trainer.train()