Update Tutorials 0.2 (#490)

Author: dario-coscia

.gitignore

@@ -139,3 +139,9 @@ cython_debug/
 
 # Lightning logs dir
 **lightning_logs
+
+# Tutorial logs dir
+**tutorial_logs
+
+# tmp dir
+**tmp*

pina/problem/zoo/supervised_problem.py

@@ -2,7 +2,7 @@
 
 from ..abstract_problem import AbstractProblem
 from ... import Condition
-from ... import Graph
+from ... import LabelTensor
 
 
 class SupervisedProblem(AbstractProblem):
@@ -22,16 +22,22 @@ class SupervisedProblem(AbstractProblem):
 
     conditions = {}
     output_variables = None
+    input_variables = None
 
-    def __init__(self, input_, output_):
+    def __init__(
+        self, input_, output_, input_variables=None, output_variables=None
+    ):
         """
         Initialize the SupervisedProblem class.
 
         :param input_: Input data of the problem.
+        :type input_: torch.Tensor | LabelTensor | Graph | Data
         :param output_: Output data of the problem.
-        :type output_: torch.Tensor | Graph
+        :type output_: torch.Tensor | LabelTensor | Graph | Data
         """
-        if isinstance(input_, Graph):
-            input_ = input_.data
+        # Set input and output variables
+        self.input_variables = input_variables
+        self.output_variables = output_variables
+        # Set the condition
         self.conditions["data"] = Condition(input=input_, target=output_)
         super().__init__()

pyproject.toml

@@ -22,7 +22,7 @@ dependencies = [
 requires-python = ">=3.8"
 
 [project.optional-dependencies]
-docs = [
+doc = [
     "sphinx>5.0",
     "sphinx_rtd_theme",
     "sphinx_copybutton",
@@ -37,8 +37,13 @@ test = [
 dev = [
     "black @ git+https://github.com/psf/black"
 ]
-tutorials = [
+tutorial = [
+    "jupyter",
     "smithers @ git+https://github.com/mathLab/smithers.git",
+    "torchvision",
+    "tensorboard",
+    "scipy",
+    "numpy",
 ]
 
 [project.urls]

tutorials/README.md

@@ -33,3 +33,4 @@ Time dependent Kuramoto Sivashinsky equation using the Averaging Neural Operator
 |---------------|-----------|
 Unstructured convolutional autoencoder via continuous convolution |[[.ipynb](tutorial4/tutorial.ipynb), [.py](tutorial4/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial4/tutorial.html)]|
 POD-RBF and POD-NN for reduced order modeling| [[.ipynb](tutorial8/tutorial.ipynb), [.py](tutorial8/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial8/tutorial.html)]|
+POD-RBF for modelling Lid Cavity| [[.ipynb](tutorial14/tutorial.ipynb), [.py](tutorial14/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial14/tutorial.html)]|