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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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22
tutorials/tutorial7/tutorial.py vendored
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@@ -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).