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Update Condition notation & domains import in tutorials

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This commit is contained in:
MatteoB30
2025-02-07 15:08:42 +01:00
committed by Nicola Demo
parent 195224794f
commit c6f1aafdec
18 changed files with 224 additions and 256 deletions
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26
tutorials/tutorial7/tutorial.ipynb vendored
View File

@@ -50,7 +50,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"id": "00d1027d-13f2-4619-9ff7-a740568f13ff",
"metadata": {},
"outputs": [],
@@ -78,7 +78,7 @@
"from pina.equation import Equation, FixedValue\n",
"from pina import Condition, Trainer\n",
"from pina.solvers import PINN\n",
"from pina.geometry import CartesianDomain"
"from pina.domain import CartesianDomain"
]
},
{
@@ -155,7 +155,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"id": "8ec0d95d-72c2-40a4-a310-21c3d6fe17d2",
"metadata": {},
"outputs": [],
@@ -188,19 +188,19 @@
"\n",
" # define the conditions for the loss (boundary conditions, equation, data)\n",
" conditions = {\n",
" 'gamma1': Condition(location=CartesianDomain({'x': [x_min, x_max],\n",
" 'bound_cond1': Condition(domain=CartesianDomain({'x': [x_min, x_max],\n",
" 'y': y_max}),\n",
" equation=FixedValue(0.0, components=['u'])),\n",
" 'gamma2': Condition(location=CartesianDomain({'x': [x_min, x_max], 'y': y_min\n",
" 'bound_cond2': Condition(domain=CartesianDomain({'x': [x_min, x_max], 'y': y_min\n",
" }),\n",
" equation=FixedValue(0.0, components=['u'])),\n",
" 'gamma3': Condition(location=CartesianDomain({'x': x_max, 'y': [y_min, y_max]\n",
" 'bound_cond3': Condition(domain=CartesianDomain({'x': x_max, 'y': [y_min, y_max]\n",
" }),\n",
" equation=FixedValue(0.0, components=['u'])),\n",
" 'gamma4': Condition(location=CartesianDomain({'x': x_min, 'y': [y_min, y_max]\n",
" 'bound_cond4': Condition(domain=CartesianDomain({'x': x_min, 'y': [y_min, y_max]\n",
" }),\n",
" equation=FixedValue(0.0, components=['u'])),\n",
" 'D': Condition(location=CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]\n",
" '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",
@@ -242,14 +242,14 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"id": "e3e0ae40-d8c6-4c08-81e8-85adc60a94e6",
"metadata": {},
"outputs": [],
"source": [
"problem.discretise_domain(20, 'grid', locations=['D'], variables=['x', 'y'])\n",
"problem.discretise_domain(1000, 'random', locations=['gamma1', 'gamma2',\n",
" 'gamma3', 'gamma4'], variables=['x', 'y'])"
"problem.discretise_domain(20, 'grid', locations=['phys_cond'], variables=['x', 'y'])\n",
"problem.discretise_domain(1000, 'random', locations=['bound_cond1', 'bound_cond2',\n",
" 'bound_cond3', 'bound_cond4'], variables=['x', 'y'])"
]
},
{
@@ -368,7 +368,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
"version": "3.12.3"
}
},
"nbformat": 4,

22
tutorials/tutorial7/tutorial.py vendored
View File

@@ -25,7 +25,7 @@
# Let's start with useful imports.
# In[1]:
# In[ ]:
## routine needed to run the notebook on Google Colab
@@ -81,7 +81,7 @@ plt.show()
# Then, we initialize the Poisson problem, that is inherited from the `SpatialProblem` and from the `InverseProblem` classes. We here have to define all the variables, and the domain where our unknown parameters ($\mu_1$, $\mu_2$) belong. Notice that the Laplace equation takes as inputs also the unknown variables, that will be treated as parameters that the neural network optimizes during the training process.
# In[4]:
# In[ ]:
### Define ranges of variables
@@ -112,19 +112,19 @@ class Poisson(SpatialProblem, InverseProblem):
# define the conditions for the loss (boundary conditions, equation, data)
conditions = {
'gamma1': Condition(location=CartesianDomain({'x': [x_min, x_max],
'bound_cond1': Condition(domain=CartesianDomain({'x': [x_min, x_max],
'y': y_max}),
equation=FixedValue(0.0, components=['u'])),
'gamma2': Condition(location=CartesianDomain({'x': [x_min, x_max], 'y': y_min
'bound_cond2': Condition(domain=CartesianDomain({'x': [x_min, x_max], 'y': y_min
}),
equation=FixedValue(0.0, components=['u'])),
'gamma3': Condition(location=CartesianDomain({'x': x_max, 'y': [y_min, y_max]
'bound_cond3': Condition(domain=CartesianDomain({'x': x_max, 'y': [y_min, y_max]
}),
equation=FixedValue(0.0, components=['u'])),
'gamma4': Condition(location=CartesianDomain({'x': x_min, 'y': [y_min, y_max]
'bound_cond4': Condition(domain=CartesianDomain({'x': x_min, 'y': [y_min, y_max]
}),
equation=FixedValue(0.0, components=['u'])),
'D': Condition(location=CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]
'phys_cond': Condition(domain=CartesianDomain({'x': [x_min, x_max], 'y': [y_min, y_max]
}),
equation=Equation(laplace_equation)),
'data': Condition(input_points=data_input.extract(['x', 'y']), output_points=data_output)
@@ -148,12 +148,12 @@ model = FeedForward(
# After that, we discretize the spatial domain.
# In[6]:
# In[ ]:
problem.discretise_domain(20, 'grid', locations=['D'], variables=['x', 'y'])
problem.discretise_domain(1000, 'random', locations=['gamma1', 'gamma2',
'gamma3', 'gamma4'], variables=['x', 'y'])
problem.discretise_domain(20, 'grid', locations=['phys_cond'], variables=['x', 'y'])
problem.discretise_domain(1000, 'random', locations=['bound_cond1', 'bound_cond2',
'bound_cond3', 'bound_cond4'], variables=['x', 'y'])
# Here, we define a simple callback for the trainer. We use this callback to save the parameters predicted by the neural network during the training. The parameters are saved every 100 epochs as `torch` tensors in a specified directory (`tmp_dir` in our case).