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fix readme examples (#599)

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Dario Coscia
2025-07-08 09:22:39 +02:00
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parent 2cb0eadac1
commit 008888fb1e
1 changed files with 4 additions and 2 deletions
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README.md
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@@ -90,20 +90,21 @@ Do you want to learn more about it? Look at our [Tutorials](https://github.com/m
### Solve Data Driven Problems ### Solve Data Driven Problems
Data driven modelling aims to learn a function that given some input data gives an output (e.g. regression, classification, ...). In PINA you can easily do this by: Data driven modelling aims to learn a function that given some input data gives an output (e.g. regression, classification, ...). In PINA you can easily do this by:
```python ```python
import torch
from pina import Trainer from pina import Trainer
from pina.model import FeedForward from pina.model import FeedForward
from pina.solver import SupervisedSolver from pina.solver import SupervisedSolver
from pina.problem.zoo import SupervisedProblem from pina.problem.zoo import SupervisedProblem
input_tensor = torch.rand((10, 1)) input_tensor = torch.rand((10, 1))
output_tensor = input_tensor.pow(3) target_tensor = input_tensor.pow(3)
# Step 1. Define problem # Step 1. Define problem
problem = SupervisedProblem(input_tensor, target_tensor) problem = SupervisedProblem(input_tensor, target_tensor)
# Step 2. Design model (you can use your favourite torch.nn.Module in here) # Step 2. Design model (you can use your favourite torch.nn.Module in here)
model = FeedForward(input_dimensions=1, output_dimensions=1, layers=[64, 64]) model = FeedForward(input_dimensions=1, output_dimensions=1, layers=[64, 64])
# Step 3. Define Solver # Step 3. Define Solver
solver = SupervisedSolver(problem, model) solver = SupervisedSolver(problem, model, use_lt=False)
# Step 4. Train # Step 4. Train
trainer = Trainer(solver, max_epochs=1000, accelerator='gpu') trainer = Trainer(solver, max_epochs=1000, accelerator='gpu')
trainer.train() trainer.train()
@@ -149,6 +150,7 @@ class SimpleODE(SpatialProblem):
# Step 1. Define problem # Step 1. Define problem
problem = SimpleODE() problem = SimpleODE()
problem.discretise_domain(n=100, mode="grid", domains=["D", "x0"])
# Step 2. Design model (you can use your favourite torch.nn.Module in here) # Step 2. Design model (you can use your favourite torch.nn.Module in here)
model = FeedForward(input_dimensions=1, output_dimensions=1, layers=[64, 64]) model = FeedForward(input_dimensions=1, output_dimensions=1, layers=[64, 64])
# Step 3. Define Solver # Step 3. Define Solver