Update Condition notation, delete error outputs and output folders

tutorials/tutorial10/tutorial.ipynb

@@ -260,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -300,8 +300,8 @@
     "class NeuralOperatorProblem(AbstractProblem):\n",
     "    input_variables = initial_cond_train.labels\n",
     "    output_variables = sol_train.labels\n",
-    "    conditions = {'data' : Condition(input_points=initial_cond_train, \n",
-    "                                     output_points=sol_train)}\n",
+    "    conditions = {'data' : Condition(input=initial_cond_train, \n",
+    "                                     target=sol_train)}\n",
     "\n",
     "\n",
     "# initialize problem\n",

tutorials/tutorial5/tutorial.ipynb

@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "8b27d283",
    "metadata": {
     "ExecuteTime": {
@@ -163,8 +163,8 @@
     "class NeuralOperatorSolver(AbstractProblem):\n",
     "    input_variables = k_train.full_labels[3]['dof']\n",
     "    output_variables = u_train.full_labels[3]['dof']\n",
-    "    conditions = {'data' : Condition(input_points=k_train, \n",
-    "                                     output_points=u_train)}\n",
+    "    conditions = {'data' : Condition(input=k_train, \n",
+    "                                     target=u_train)}\n",
     "# make problem\n",
     "problem = NeuralOperatorSolver()"
    ]

tutorials/tutorial7/tutorial.ipynb

@@ -85,12 +85,12 @@
    "id": "5138afdf-bff6-46bf-b423-a22673190687",
    "metadata": {},
    "source": [
-    "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`)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "2c55d972-09a9-41de-9400-ba051c28cdcb",
    "metadata": {},
    "outputs": [
@@ -104,8 +104,8 @@
     }
    ],
    "source": [
-    "data_output = torch.load('data/pinn_solution_0.5_0.5').detach()\n",
-    "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()\n",
+    "data_input = torch.load('data/pts_0.5_0.5', weights_only = False)"
    ]
   },
   {
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "8ec0d95d-72c2-40a4-a310-21c3d6fe17d2",
    "metadata": {},
    "outputs": [],
@@ -216,7 +216,7 @@
     "        'phys_cond': Condition(domain=CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]\n",
     "            }),\n",
     "        equation=Equation(laplace_equation)),\n",
-    "        'data': Condition(input_points=data_input.extract(['x', 'y']), output_points=data_output)\n",
+    "        'data': Condition(input=data_input.extract(['x', 'y']), target=data_output)\n",
     "    }\n",
     "\n",
     "problem = Poisson()"

tutorials/tutorial8/tutorial.ipynb

@@ -176,12 +176,12 @@
    "id": "c46410fa-2718-4fc9-977a-583fe2390028",
    "metadata": {},
    "source": [
-    "It is now time to define the problem! We inherit from `ParametricProblem` (since the space invariant typically of this methodology), just defining a simple *input-output* condition."
+    "It is now time to define the problem! We inherit from `ParametricProblem` (since the space invariance typical of this methodology), just defining a simple *input-target* condition."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "55cef553-7495-401d-9d17-1acff8ec5953",
    "metadata": {},
    "outputs": [],
@@ -191,7 +191,7 @@
     "    parameter_domain = CartesianDomain({'mu': [0, 100]})\n",
     "\n",
     "    conditions = {\n",
-    "        'io': Condition(input_points=p_train, output_points=u_train)\n",
+    "        'io': Condition(input=p_train, target=u_train)\n",
     "    }\n",
     "\n",
     "poisson_problem = SnapshotProblem()"