Update tutorials (#463)

---------

Co-authored-by: Dario Coscia <93731561+dario-coscia@users.noreply.github.com>

tutorials/tutorial7/tutorial.py

@@ -25,7 +25,7 @@
 
 # Let's start with useful imports.
 
-# In[ ]:
+# In[1]:
 
 
 ## routine needed to run the notebook on Google Colab
@@ -42,25 +42,27 @@ if IN_COLAB:
   get_ipython().system('wget "https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial7/data/pts_0.5_0.5" -O "data/pts_0.5_0.5"')
   
 import matplotlib.pyplot as plt
-plt.style.use('tableau-colorblind10')
 import torch
-from pytorch_lightning.callbacks import Callback
+import warnings
+
 from pina.problem import SpatialProblem, InverseProblem
-from pina.operators import laplacian
+from pina.operator import laplacian
 from pina.model import FeedForward
 from pina.equation import Equation, FixedValue
 from pina import Condition, Trainer
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.domain import CartesianDomain
 
+warnings.filterwarnings('ignore')
 
-# Then, we import the pre-saved data, for ($\mu_1$, $\mu_2$)=($0.5$, $0.5$). These two values are the optimal parameters that we want to find through the neural network training. In particular, we import the `input_points`(the spatial coordinates), and the `output_points` (the corresponding $u$ values evaluated at the `input_points`).
+
+# Then, we import the pre-saved data, for ($\mu_1$, $\mu_2$)=($0.5$, $0.5$). These two values are the optimal parameters that we want to find through the neural network training. In particular, we import the `input` points (the spatial coordinates), and the `target` points (the corresponding $u$ values evaluated at the `input`).
 
 # In[2]:
 
 
-data_output = torch.load('data/pinn_solution_0.5_0.5').detach()
-data_input = torch.load('data/pts_0.5_0.5')
+data_output = torch.load('data/pinn_solution_0.5_0.5', weights_only = False).detach()
+data_input = torch.load('data/pts_0.5_0.5', weights_only = False)
 
 
 # Moreover, let's plot also the data points and the reference solution: this is the expected output of the neural network.
@@ -81,7 +83,7 @@ plt.show()
 
 # Then, we initialize the Poisson problem, that is inherited from the `SpatialProblem` and from the `InverseProblem` classes. We here have to define all the variables, and the domain where our unknown parameters ($\mu_1$, $\mu_2$) belong. Notice that the Laplace equation takes as inputs also the unknown variables, that will be treated as parameters that the neural network optimizes during the training process.
 
-# In[ ]:
+# In[4]:
 
 
 ### Define ranges of variables
@@ -127,7 +129,7 @@ class Poisson(SpatialProblem, InverseProblem):
         'phys_cond': Condition(domain=CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]
             }),
         equation=Equation(laplace_equation)),
-        'data': Condition(input_points=data_input.extract(['x', 'y']), output_points=data_output)
+        'data': Condition(input=data_input.extract(['x', 'y']), target=data_output)
     }
 
 problem = Poisson()
@@ -148,12 +150,12 @@ model = FeedForward(
 
 # After that, we discretize the spatial domain.
 
-# In[ ]:
+# In[6]:
 
 
-problem.discretise_domain(20, 'grid', locations=['phys_cond'], variables=['x', 'y'])
-problem.discretise_domain(1000, 'random', locations=['bound_cond1', 'bound_cond2',
-    'bound_cond3', 'bound_cond4'], variables=['x', 'y'])
+problem.discretise_domain(20, 'grid', domains=['phys_cond'])
+problem.discretise_domain(1000, 'random', domains=['bound_cond1', 'bound_cond2',
+    'bound_cond3', 'bound_cond4'])
 
 
 # Here, we define a simple callback for the trainer. We use this callback to save the parameters predicted by the neural network during the training. The parameters are saved every 100 epochs as `torch` tensors in a specified directory (`tmp_dir` in our case).
@@ -162,6 +164,7 @@ problem.discretise_domain(1000, 'random', locations=['bound_cond1', 'bound_cond2
 # In[7]:
 
 
+from lightning.pytorch.callbacks import Callback
 # temporary directory for saving logs of training
 tmp_dir = "tmp_poisson_inverse"
 
@@ -176,15 +179,19 @@ class SaveParameters(Callback):
 
 # Then, we define the `PINN` object and train the solver using the `Trainer`.
 
-# In[ ]:
+# In[8]:
 
 
 ### train the problem with PINN
+from pina.optim import TorchOptimizer
 max_epochs = 5000
-pinn = PINN(problem, model, optimizer_kwargs={'lr':0.005})
+pinn = PINN(problem, model, optimizer=TorchOptimizer(torch.optim.Adam, lr=0.005))
 # define the trainer for the solver
 trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=max_epochs,
-        default_root_dir=tmp_dir, callbacks=[SaveParameters()])
+        default_root_dir=tmp_dir, enable_model_summary=False, callbacks=[SaveParameters()],
+        train_size=1.0,
+        val_size=0.0,
+        test_size=0.0)
 trainer.train()
 
 
@@ -196,7 +203,7 @@ trainer.train()
 epochs_saved = range(99, max_epochs, 100)
 parameters = torch.empty((int(max_epochs/100), 2))
 for i, epoch in enumerate(epochs_saved):
-    params_torch = torch.load('{}/parameters_epoch{}'.format(tmp_dir, epoch))
+    params_torch = torch.load('{}/parameters_epoch{}'.format(tmp_dir, epoch),weights_only = False)
     for e, var in enumerate(pinn.problem.unknown_variables):         
         parameters[i, e] = params_torch[var].data