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

Automatize Tutorials html, py files creation (#496)

Browse Source 
* workflow to export tutorials

---------
This commit is contained in:
Dario Coscia
2025-03-15 11:01:19 +01:00
committed by Nicola Demo
parent aea24d0bee
commit 0146155c9b
51 changed files with 140529 additions and 440 deletions
Download Patch File Download Diff File Expand all files Collapse all files

42
tutorials/tutorial2/tutorial.ipynb vendored
View File

@@ -23,12 +23,13 @@
"source": [
"## routine needed to run the notebook on Google Colab\n",
"try:\n",
" import google.colab\n",
" IN_COLAB = True\n",
" import google.colab\n",
"\n",
" IN_COLAB = True\n",
"except:\n",
" IN_COLAB = False\n",
" IN_COLAB = False\n",
"if IN_COLAB:\n",
" !pip install \"pina-mathlab\"\n",
" !pip install \"pina-mathlab\"\n",
"\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
@@ -39,7 +40,7 @@
"from pina.solver import PINN\n",
"from torch.nn import Softplus\n",
"\n",
"warnings.filterwarnings('ignore')"
"warnings.filterwarnings(\"ignore\")"
]
},
{
@@ -186,8 +187,7 @@
" train_size=0.8, # set train size\n",
" val_size=0.0, # set validation size\n",
" test_size=0.2, # set testing size\n",
" shuffle=True, # shuffle the data\n",
" \n",
" shuffle=True, # shuffle the data\n",
")\n",
"\n",
"# train\n",
@@ -348,7 +348,7 @@
"\n",
" def forward(self, pts):\n",
" x, y = pts.extract([\"x\"]), pts.extract([\"y\"])\n",
" f = 2 *torch.pi**2 * torch.sin(x * torch.pi) * torch.sin(y * torch.pi)\n",
" f = 2 * torch.pi**2 * torch.sin(x * torch.pi) * torch.sin(y * torch.pi)\n",
" return LabelTensor(f, [\"feat\"])\n",
"\n",
"\n",
@@ -487,28 +487,36 @@
"source": [
"class SinSinAB(torch.nn.Module):\n",
" \"\"\" \"\"\"\n",
"\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.alpha = torch.nn.Parameter(torch.tensor([1.0]))\n",
" self.beta = torch.nn.Parameter(torch.tensor([1.0]))\n",
"\n",
" def forward(self, x):\n",
" t = (\n",
" self.beta*torch.sin(self.alpha*x.extract(['x'])*torch.pi)*\n",
" torch.sin(self.alpha*x.extract(['y'])*torch.pi)\n",
" t = (\n",
" self.beta\n",
" * torch.sin(self.alpha * x.extract([\"x\"]) * torch.pi)\n",
" * torch.sin(self.alpha * x.extract([\"y\"]) * torch.pi)\n",
" )\n",
" return LabelTensor(t, ['b*sin(a*x)sin(a*y)'])\n",
" return LabelTensor(t, [\"b*sin(a*x)sin(a*y)\"])\n",
"\n",
"\n",
"# make model + solver + trainer\n",
"model_learn = FeedForwardWithExtraFeatures(\n",
" input_dimensions=len(problem.input_variables) + 1, #we add one as also we consider the extra feature dimension\n",
" input_dimensions=len(problem.input_variables)\n",
" + 1, # we add one as also we consider the extra feature dimension\n",
" output_dimensions=len(problem.output_variables),\n",
" func=Softplus,\n",
" layers=[10, 10],\n",
" extra_features=SinSinAB())\n",
" extra_features=SinSinAB(),\n",
")\n",
"\n",
"pinn_learn = PINN(problem, model_learn, optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006,weight_decay=1e-8))\n",
"pinn_learn = PINN(\n",
" problem,\n",
" model_learn,\n",
" optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8),\n",
")\n",
"trainer_learn = Trainer(\n",
" solver=pinn_learn, # setting the solver, i.e. PINN\n",
" max_epochs=1000, # setting max epochs in training\n",
@@ -649,14 +657,14 @@
],
"source": [
"# test error base pinn\n",
"print('PINN')\n",
"print(\"PINN\")\n",
"trainer_base.test()\n",
"# test error extra features pinn\n",
"print(\"PINN with extra features\")\n",
"trainer_feat.test()\n",
"# test error learnable extra features pinn\n",
"print(\"PINN with learnable extra features\")\n",
"_=trainer_learn.test()"
"_ = trainer_learn.test()"
]
},
{