70 lines
1.9 KiB
Python
70 lines
1.9 KiB
Python
import argparse
|
|
import sys
|
|
import numpy as np
|
|
import torch
|
|
from torch.nn import ReLU, Tanh, Softplus
|
|
|
|
from pina import PINN, LabelTensor, Plotter
|
|
from pina.model import FeedForward
|
|
from pina.adaptive_functions import AdaptiveSin, AdaptiveCos, AdaptiveTanh
|
|
from problems.poisson import Poisson
|
|
|
|
|
|
class myFeature(torch.nn.Module):
|
|
"""
|
|
Feature: sin(x)
|
|
"""
|
|
|
|
def __init__(self):
|
|
super(myFeature, self).__init__()
|
|
|
|
def forward(self, x):
|
|
return torch.sin(x[:, 0]*torch.pi) * torch.sin(x[:, 1]*torch.pi)
|
|
|
|
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)
|
|
args = parser.parse_args()
|
|
|
|
feat = [myFeature()] if args.features else []
|
|
|
|
poisson_problem = Poisson()
|
|
model = FeedForward(
|
|
layers=[10, 10],
|
|
output_variables=poisson_problem.output_variables,
|
|
input_variables=poisson_problem.input_variables,
|
|
func=Softplus,
|
|
extra_features=feat
|
|
)
|
|
|
|
pinn = PINN(
|
|
poisson_problem,
|
|
model,
|
|
lr=0.003,
|
|
error_norm='mse',
|
|
regularizer=1e-8,
|
|
lr_accelerate=None)
|
|
|
|
if args.s:
|
|
|
|
pinn.span_pts(20, 'grid', ['D'])
|
|
pinn.span_pts(20, 'grid', ['gamma1', 'gamma2', 'gamma3', 'gamma4'])
|
|
#pinn.plot_pts()
|
|
pinn.train(1000, 100)
|
|
with open('poisson_history_{}_{}.txt'.format(args.id_run, args.features), 'w') as file_:
|
|
for i, losses in enumerate(pinn.history):
|
|
file_.write('{} {}\n'.format(i, sum(losses)))
|
|
pinn.save_state('pina.poisson')
|
|
|
|
else:
|
|
pinn.load_state('pina.poisson')
|
|
plotter = Plotter()
|
|
plotter.plot(pinn)
|
|
|
|
|