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PINA/tests/test_weighting/test_self_adaptive_weighting.py
2025-09-08 14:46:33 +02:00

38 lines
1.1 KiB
Python

import pytest
from pina import Trainer
from pina.solver import PINN
from pina.model import FeedForward
from pina.loss import SelfAdaptiveWeighting
from pina.problem.zoo import Poisson2DSquareProblem
# Initialize problem and model
problem = Poisson2DSquareProblem()
problem.discretise_domain(10)
model = FeedForward(len(problem.input_variables), len(problem.output_variables))
@pytest.mark.parametrize("k", [10, 100, 1000])
def test_constructor(k):
SelfAdaptiveWeighting(k=k)
# Should fail if k is not an integer
with pytest.raises(AssertionError):
SelfAdaptiveWeighting(k=1.5)
# Should fail if k is not > 0
with pytest.raises(AssertionError):
SelfAdaptiveWeighting(k=0)
# Should fail if k is not > 0
with pytest.raises(AssertionError):
SelfAdaptiveWeighting(k=-3)
@pytest.mark.parametrize("k", [2, 3])
def test_train_aggregation(k):
weighting = SelfAdaptiveWeighting(k=k)
solver = PINN(problem=problem, model=model, weighting=weighting)
trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu")
trainer.train()