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Documentation for v0.1 version (#199)

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* 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>
This commit is contained in:
Dario Coscia
2023-11-08 14:39:00 +01:00
committed by Nicola Demo
parent 3f9305d475
commit 8b7b61b3bd
144 changed files with 2741 additions and 1766 deletions
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20
tests/test_callbacks/test_adaptive_refinment_callbacks.py
View File

@@ -1,5 +1,3 @@
from pina.callbacks import R3Refinement
import torch
import pytest
@@ -7,7 +5,8 @@ import pytest
from pina.problem import SpatialProblem
from pina.operators import laplacian
from pina.geometry import CartesianDomain
from pina import Condition, LabelTensor, PINN
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
@@ -15,15 +14,17 @@ 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))
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]})
@@ -55,7 +56,8 @@ 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))
model = FeedForward(len(poisson_problem.input_variables),
len(poisson_problem.output_variables))
# make the solver
solver = PINN(problem=poisson_problem, model=model)
@@ -64,8 +66,10 @@ 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 = Trainer(solver=solver,
callbacks=[R3Refinement(sample_every=1)],
max_epochs=5)
trainer.train()

33
tests/test_callbacks/test_optimizer_callbacks.py
View File

@@ -1,5 +1,3 @@
from pina.callbacks import SwitchOptimizer
import torch
import pytest
@@ -7,7 +5,8 @@ import pytest
from pina.problem import SpatialProblem
from pina.operators import laplacian
from pina.geometry import CartesianDomain
from pina import Condition, LabelTensor, PINN
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
@@ -15,15 +14,17 @@ 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))
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]})
@@ -55,7 +56,8 @@ 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))
model = FeedForward(len(poisson_problem.input_variables),
len(poisson_problem.output_variables))
# make the solver
solver = PINN(problem=poisson_problem, model=model)
@@ -63,19 +65,24 @@ solver = PINN(problem=poisson_problem, model=model)
def test_switch_optimizer_constructor():
SwitchOptimizer(new_optimizers=torch.optim.Adam,
new_optimizers_kwargs={'lr':0.01},
new_optimizers_kwargs={'lr': 0.01},
epoch_switch=10)
with pytest.raises(ValueError):
SwitchOptimizer(new_optimizers=[torch.optim.Adam, torch.optim.Adam],
new_optimizers_kwargs=[{'lr':0.01}],
new_optimizers_kwargs=[{
'lr': 0.01
}],
epoch_switch=10)
def test_switch_optimizer_routine():
# make the trainer
trainer = Trainer(solver=solver, callbacks=[SwitchOptimizer(new_optimizers=torch.optim.LBFGS,
new_optimizers_kwargs={'lr':0.01},
epoch_switch=3)], max_epochs=5)
trainer = Trainer(solver=solver,
callbacks=[
SwitchOptimizer(new_optimizers=torch.optim.LBFGS,
new_optimizers_kwargs={'lr': 0.01},
epoch_switch=3)
],
max_epochs=5)
trainer.train()