folivo/PINA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix examples (#21)

Browse Source
This commit is contained in:
Nicola Demo
2022-07-21 13:41:59 +02:00
committed by GitHub
parent 62f203fcc3
commit e8c2f87460
14 changed files with 63 additions and 67 deletions
Download Patch File Download Diff File Expand all files Collapse all files

13
examples/first_order_ode.py
View File

@@ -1,15 +1,12 @@
import argparse
import torch
from torch.nn import ReLU, Tanh, Softplus, PReLU
from torch.nn import Softplus
from pina.problem import SpatialProblem, ParametricProblem
from pina.operators import nabla, grad, div
from pina.model import FeedForward, DeepONet
from pina import Condition, Span, LabelTensor, Plotter, PINN
import matplotlib
matplotlib.use('Qt5Agg')
from pina.problem import SpatialProblem
from pina.operators import grad
from pina.model import FeedForward
from pina import Condition, Span, Plotter, PINN
class FirstOrderODE(SpatialProblem):

5
examples/problems/burgers.py
View File

@@ -8,10 +8,9 @@ from pina.span import Span
class Burgers1D(TimeDependentProblem, SpatialProblem):
spatial_variables = ['x']
temporal_variable = ['t']
output_variables = ['u']
domain = Span({'x': [-1, 1], 't': [0, 1]})
spatial_domain = Span({'x': [-1, 1]})
temporal_domain = Span({'t': [0, 1]})
def burger_equation(input_, output_):
du = grad(output_, input_)

6
examples/problems/parametric_elliptic_optimal_control_alpha_variable.py
View File

@@ -16,11 +16,9 @@ class ParametricEllipticOptimalControl(SpatialProblem, ParametricProblem):
x_range = [xmin, xmax]
y_range = [ymin, ymax]
spatial_variables = ['x1', 'x2']
parameters = ['mu', 'alpha']
output_variables = ['u', 'p', 'y']
domain = Span({
'x1': x_range, 'x2': y_range, 'mu': mu_range, 'alpha': a_range})
spatial_domain = Span({'x1': x_range, 'x2': y_range})
parameter_domain = Span({'mu': mu_range, 'alpha': a_range})
def term1(input_, output_):

5
examples/problems/parametric_poisson.py
View File

@@ -7,10 +7,9 @@ from pina import Span, Condition
class ParametricPoisson(SpatialProblem, ParametricProblem):
spatial_variables = ['x', 'y']
parameters = ['mu1', 'mu2']
output_variables = ['u']
domain = Span({'x': [-1, 1], 'y': [-1, 1]})
spatial_domain = Span({'x': [-1, 1], 'y': [-1, 1]})
parameter_domain = Span({'mu1': [-1, 1], 'mu2': [-1, 1]})
def laplace_equation(input_, output_):
force_term = torch.exp(

11
examples/problems/poisson.py
View File

@@ -8,9 +8,8 @@ from pina import Condition, Span
class Poisson(SpatialProblem):
spatial_variables = ['x', 'y']
output_variables = ['u']
domain = Span({'x': [0, 1], 'y': [0, 1]})
spatial_domain = Span({'x': [0, 1], 'y': [0, 1]})
def laplace_equation(input_, output_):
force_term = (torch.sin(input_.extract(['x'])*torch.pi) *
@@ -30,7 +29,11 @@ class Poisson(SpatialProblem):
'D': Condition(Span({'x': [0, 1], 'y': [0, 1]}), laplace_equation),
}
def poisson_sol(self, x, y):
return -(np.sin(x*np.pi)*np.sin(y*np.pi))/(2*np.pi**2)
def poisson_sol(self, pts):
return -(
torch.sin(pts.extract(['x'])*torch.pi)*
torch.sin(pts.extract(['y'])*torch.pi)
)/(2*torch.pi**2)
#return -(np.sin(x*np.pi)*np.sin(y*np.pi))/(2*np.pi**2)
truth_solution = poisson_sol

3
examples/problems/stokes.py
View File

@@ -8,9 +8,8 @@ from pina import Condition, Span, LabelTensor
class Stokes(SpatialProblem):
spatial_variables = ['x', 'y']
output_variables = ['ux', 'uy', 'p']
domain = Span({'x': [-2, 2], 'y': [-1, 1]})
spatial_domain = Span({'x': [-2, 2], 'y': [-1, 1]})
def momentum(input_, output_):
nabla_ = torch.hstack((LabelTensor(nabla(output_.extract(['ux']), input_), ['x']),

6
examples/run_burgers.py
View File

@@ -4,7 +4,7 @@ from torch.nn import Softplus
from pina import PINN, Plotter, LabelTensor
from pina.model import FeedForward
from burger2 import Burgers1D
from problems.burgers import Burgers1D
class myFeature(torch.nn.Module):
@@ -49,8 +49,8 @@ if __name__ == "__main__":
if args.s:
pinn.span_pts(
{'n': 200, 'mode': 'random', 'variables': 't'},
{'n': 20, 'mode': 'random', 'variables': 'x'},
{'n': 200, 'mode': 'grid', 'variables': 't'},
{'n': 20, 'mode': 'grid', 'variables': 'x'},
locations=['D'])
pinn.span_pts(150, 'random', location=['gamma1', 'gamma2', 't0'])
pinn.train(5000, 100)

17
examples/run_parametric_elliptic_optimal_control_alpha.py
View File

@@ -1,11 +1,13 @@
import argparse
import numpy as np
import torch
from torch.nn import ReLU, Tanh, Softplus
from torch.nn import Softplus
from pina import PINN, LabelTensor, Plotter
from pina.model import MultiFeedForward
from problems.parametric_elliptic_optimal_control_alpha_variable import (
ParametricEllipticOptimalControl)
from pina import PINN, LabelTensor
from parametric_elliptic_optimal_control_alpha_variable2 import ParametricEllipticOptimalControl
from pina.model import MultiFeedForward, FeedForward
class myFeature(torch.nn.Module):
"""
@@ -31,7 +33,6 @@ class CustomMultiDFF(MultiFeedForward):
return out.append(p)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run PINA")
@@ -71,8 +72,12 @@ if __name__ == "__main__":
{'variables': ['mu', 'alpha'], 'mode': 'grid', 'n': 5},
locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
pinn.train(10000, 20)
pinn.train(1000, 20)
pinn.save_state('pina.ocp')
else:
pinn.load_state('pina.ocp')
plotter = Plotter()
plotter.plot(pinn, components='y', fixed_variables={'alpha': 0.01, 'mu': 1.0})
plotter.plot(pinn, components='u_param', fixed_variables={'alpha': 0.01, 'mu': 1.0})
plotter.plot(pinn, components='p', fixed_variables={'alpha': 0.01, 'mu': 1.0})

11
examples/run_parametric_poisson.py
View File

@@ -2,8 +2,8 @@ import argparse
import torch
from torch.nn import Softplus
from pina import Plotter, LabelTensor, PINN
from parametric_poisson2 import ParametricPoisson
from pina.model import FeedForward
from problems.parametric_poisson import ParametricPoisson
class myFeature(torch.nn.Module):
@@ -43,11 +43,7 @@ if __name__ == "__main__":
extra_features=feat
)
pinn = PINN(
poisson_problem,
model,
lr=0.006,
regularizer=1e-6)
pinn = PINN(poisson_problem, model, lr=0.006, regularizer=1e-6)
if args.s:
@@ -65,4 +61,5 @@ if __name__ == "__main__":
else:
pinn.load_state('pina.poisson_param')
plotter = Plotter()
plotter.plot(pinn, component='u', parametric=True, params_value=0)
plotter.plot(pinn, fixed_variables={'mu1': 0, 'mu2': 1}, levels=21)
plotter.plot(pinn, fixed_variables={'mu1': 1, 'mu2': -1}, levels=21)

6
examples/run_poisson.py
View File

@@ -7,7 +7,7 @@ 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 poisson2 import Poisson
from problems.poisson import Poisson
class myFeature(torch.nn.Module):
@@ -61,6 +61,4 @@ if __name__ == "__main__":
else:
pinn.load_state('pina.poisson')
plotter = Plotter()
plotter.plot(pinn, component='u')
plotter.plot(pinn)

11
examples/run_stokes.py
View File

@@ -36,9 +36,8 @@ if __name__ == "__main__":
if args.s:
pinn.span_pts(200, mode_spatial='grid', locations=['gamma_top', 'gamma_bot', 'gamma_in', 'gamma_out'])
pinn.span_pts(2000, mode_spatial='random', locations=['D'])
pinn.plot_pts()
pinn.span_pts(200, 'grid', locations=['gamma_top', 'gamma_bot', 'gamma_in', 'gamma_out'])
pinn.span_pts(2000, 'random', locations=['D'])
pinn.train(10000, 100)
with open('stokes_history_{}.txt'.format(args.id_run), 'w') as file_:
for i, losses in enumerate(pinn.history):
@@ -48,8 +47,8 @@ if __name__ == "__main__":
else:
pinn.load_state('pina.stokes')
plotter = Plotter()
plotter.plot(pinn, component='ux')
plotter.plot(pinn, component='uy')
plotter.plot(pinn, component='p')
plotter.plot(pinn, components='ux')
plotter.plot(pinn, components='uy')
plotter.plot(pinn, components='p')

32
pina/plotter.py
View File

@@ -50,22 +50,23 @@ class Plotter:
plt.legend()
plt.show()
def _1d_plot(self, pts, pred, method, truth_solution=None):
def _1d_plot(self, pts, pred, method, truth_solution=None, **kwargs):
"""
"""
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))
ax.plot(pts, pred.detach())
ax.plot(pts, pred.detach(), **kwargs)
if truth_solution:
truth_output = truth_solution(pts).float()
ax.plot(pts, truth_output.detach())
ax.plot(pts, truth_output.detach(), **kwargs)
plt.xlabel(pts.labels[0])
plt.ylabel(pred.labels[0])
plt.show()
def _2d_plot(self, pts, pred, v, res, method, truth_solution=None):
def _2d_plot(self, pts, pred, v, res, method, truth_solution=None,
**kwargs):
"""
"""
@@ -73,27 +74,29 @@ class Plotter:
pred_output = pred.reshape(res, res)
if truth_solution:
truth_output = truth_solution(*pts.T).float().reshape(res, res)
truth_output = truth_solution(pts).float().reshape(res, res)
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))
cb = getattr(ax[0], method)(*grids, pred_output.detach())
cb = getattr(ax[0], method)(*grids, pred_output.detach(), **kwargs)
fig.colorbar(cb, ax=ax[0])
cb = getattr(ax[1], method)(*grids, truth_output.detach())
cb = getattr(ax[1], method)(*grids, truth_output.detach(), **kwargs)
fig.colorbar(cb, ax=ax[1])
cb = getattr(ax[2], method)(*grids,
(truth_output-pred_output).detach())
(truth_output-pred_output).detach(),
**kwargs)
fig.colorbar(cb, ax=ax[2])
else:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 6))
cb = getattr(ax, method)(*grids, pred_output.detach())
cb = getattr(ax, method)(*grids, pred_output.detach(), **kwargs)
fig.colorbar(cb, ax=ax)
def plot(self, pinn, components, fixed_variables={}, method='contourf',
res=256, filename=None):
def plot(self, pinn, components=None, fixed_variables={}, method='contourf',
res=256, filename=None, **kwargs):
"""
"""
if components is None:
components = [pinn.problem.output_variables]
v = [
var for var in pinn.problem.input_variables
if var not in fixed_variables.keys()
@@ -112,10 +115,11 @@ class Plotter:
truth_solution = getattr(pinn.problem, 'truth_solution', None)
if len(v) == 1:
self._1d_plot(pts, predicted_output, method, truth_solution)
self._1d_plot(pts, predicted_output, method, truth_solution,
**kwargs)
elif len(v) == 2:
self._2d_plot(pts, predicted_output, v, res, method,
truth_solution)
truth_solution, **kwargs)
if filename:
plt.title('Output {} with parameter {}'.format(components,

2
pina/problem/abstract_problem.py
View File

@@ -39,8 +39,6 @@ class AbstractProblem(metaclass=ABCMeta):
else:
raise RuntimeError('different domains')
@input_variables.setter
def input_variables(self, variables):
raise RuntimeError

2
pina/problem/timedep_problem.py
View File

@@ -10,5 +10,5 @@ class TimeDependentProblem(AbstractProblem):
pass
@property
def temporal_variables(self):
def temporal_variable(self):
return self.temporal_domain.variables