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Update tutorials 1 through 12 to current version 0.2

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This commit is contained in:
Matteo Bertocchi
2025-02-26 16:21:12 +01:00
committed by Nicola Demo
parent 8b797d589a
commit d83ca3af6e
82 changed files with 1074 additions and 1224 deletions
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29
tutorials/tutorial10/tutorial.ipynb vendored
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14
tutorials/tutorial10/tutorial.py vendored
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@@ -37,7 +37,7 @@ from scipy import io
from pina import Condition, LabelTensor
from pina.problem import AbstractProblem
from pina.model import AveragingNeuralOperator
from pina.solvers import SupervisedSolver
from pina.solver import SupervisedSolver
from pina.trainer import Trainer
@@ -81,7 +81,7 @@ from pina.trainer import Trainer
# load data
data=io.loadmat("dat/Data_KS.mat")
data=io.loadmat("data/Data_KS.mat")
# converting to label tensor
initial_cond_train = LabelTensor(torch.tensor(data['initial_cond_train'], dtype=torch.float), ['t','x','u0'])
@@ -203,7 +203,7 @@ model = AveragingNeuralOperator(lifting_net=lifting_net,
# We will now focus on solving the KS equation using the `SupervisedSolver` class
# and the `AveragingNeuralOperator` model. As done in the [FNO tutorial](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial5/tutorial.ipynb) we now create the `NeuralOperatorProblem` class with `AbstractProblem`.
# In[6]:
# In[5]:
# expected running time ~ 1 minute
@@ -218,7 +218,7 @@ class NeuralOperatorProblem(AbstractProblem):
# initialize problem
problem = NeuralOperatorProblem()
# initialize solver
solver = SupervisedSolver(problem=problem, model=model,optimizer_kwargs={"lr":0.001})
solver = SupervisedSolver(problem=problem, model=model)
# train, only CPU and avoid model summary at beginning of training (optional)
trainer = Trainer(solver=solver, max_epochs=40, accelerator='cpu', enable_model_summary=False, log_every_n_steps=-1, batch_size=5) # we train on CPU and avoid model summary at beginning of training (optional)
trainer.train()
@@ -226,7 +226,7 @@ trainer.train()
# We can now see some plots for the solutions
# In[7]:
# In[6]:
sample_number = 2
@@ -239,10 +239,10 @@ plot_trajectory(coords=initial_cond_test[sample_number].extract(['x', 't']),
# As we can see we can obtain nice result considering the small trainint time and the difficulty of the problem!
# Let's see how the training and testing error:
# In[8]:
# In[7]:
from pina.loss.loss_interface import PowerLoss
from pina.loss import PowerLoss
error_metric = PowerLoss(p=2) # we use the MSE loss