8b7b61b3bd
* Adding Equations, solving typos * improve _code.rst * the team rst and restuctore index.rst * fixing errors --------- Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
from pina.callbacks import R3Refinement
|
|
import torch
|
|
import pytest
|
|
|
|
from pina.problem import SpatialProblem
|
|
from pina.operators import laplacian
|
|
from pina.geometry import CartesianDomain
|
|
from pina import Condition, LabelTensor
|
|
from pina.solvers import PINN
|
|
from pina.trainer import Trainer
|
|
from pina.model import FeedForward
|
|
from pina.equation.equation import Equation
|
|
from pina.equation.equation_factory import FixedValue
|
|
|
|
|
|
def laplace_equation(input_, output_):
|
|
force_term = (torch.sin(input_.extract(['x']) * torch.pi) *
|
|
torch.sin(input_.extract(['y']) * torch.pi))
|
|
delta_u = laplacian(output_.extract(['u']), input_)
|
|
return delta_u - force_term
|
|
|
|
|
|
my_laplace = Equation(laplace_equation)
|
|
in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y'])
|
|
out_ = LabelTensor(torch.tensor([[0.]]), ['u'])
|
|
|
|
|
|
class Poisson(SpatialProblem):
|
|
output_variables = ['u']
|
|
spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
|
|
|
|
conditions = {
|
|
'gamma1': Condition(
|
|
location=CartesianDomain({'x': [0, 1], 'y': 1}),
|
|
equation=FixedValue(0.0)),
|
|
'gamma2': Condition(
|
|
location=CartesianDomain({'x': [0, 1], 'y': 0}),
|
|
equation=FixedValue(0.0)),
|
|
'gamma3': Condition(
|
|
location=CartesianDomain({'x': 1, 'y': [0, 1]}),
|
|
equation=FixedValue(0.0)),
|
|
'gamma4': Condition(
|
|
location=CartesianDomain({'x': 0, 'y': [0, 1]}),
|
|
equation=FixedValue(0.0)),
|
|
'D': Condition(
|
|
input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']),
|
|
equation=my_laplace),
|
|
# 'data': Condition(
|
|
# input_points=in_,
|
|
# output_points=out_)
|
|
}
|
|
|
|
|
|
# make the problem
|
|
poisson_problem = Poisson()
|
|
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
|
n = 10
|
|
poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
|
|
model = FeedForward(len(poisson_problem.input_variables),
|
|
len(poisson_problem.output_variables))
|
|
|
|
# make the solver
|
|
solver = PINN(problem=poisson_problem, model=model)
|
|
|
|
|
|
def test_r3constructor():
|
|
R3Refinement(sample_every=10)
|
|
|
|
|
|
def test_r3refinment_routine():
|
|
# make the trainer
|
|
trainer = Trainer(solver=solver,
|
|
callbacks=[R3Refinement(sample_every=1)],
|
|
max_epochs=5)
|
|
trainer.train()
|