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Automatize Tutorials html, py files creation (#496)

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* 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
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32
tutorials/tutorial12/tutorial.ipynb vendored
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@@ -53,16 +53,17 @@
"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",
"\n",
"#useful imports\n",
"# useful imports\n",
"from pina import Condition\n",
"from pina.problem import SpatialProblem, TimeDependentProblem\n",
"from pina.equation import Equation, FixedValue\n",
@@ -166,22 +167,23 @@
"outputs": [],
"source": [
"class Burgers1DEquation(Equation):\n",
" \n",
" def __init__(self, nu = 0.):\n",
"\n",
" def __init__(self, nu=0.0):\n",
" \"\"\"\n",
" Burgers1D class. This class can be\n",
" used to enforce the solution u to solve the viscous Burgers 1D Equation.\n",
" \n",
"\n",
" :param torch.float32 nu: the viscosity coefficient. Default value is set to 0.\n",
" \"\"\"\n",
" self.nu = nu \n",
" \n",
" def equation(input_, output_):\n",
" return grad(output_, input_, d='t') +\\\n",
" output_*grad(output_, input_, d='x') -\\\n",
" self.nu*laplacian(output_, input_, d='x')\n",
" self.nu = nu\n",
"\n",
" def equation(input_, output_):\n",
" return (\n",
" grad(output_, input_, d=\"t\")\n",
" + output_ * grad(output_, input_, d=\"x\")\n",
" - self.nu * laplacian(output_, input_, d=\"x\")\n",
" )\n",
"\n",
" \n",
" super().__init__(equation)"
]
},