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Update Condition notation, delete error outputs and output folders

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MatteB03
2025-03-07 14:23:55 +01:00
committed by Nicola Demo
parent 0297344914
commit 2aac9d1ba7
13 changed files with 84 additions and 121762 deletions
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tutorials/tutorial7/data/pinn_solution_0.5_0.5 vendored
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tutorials/tutorial7/data/pts_0.5_0.5 vendored
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tutorials/tutorial7/tutorial.ipynb vendored
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@@ -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()"