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fix connection problem.zoo

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This commit is contained in:
Dario Coscia
2025-05-16 14:04:49 +02:00
committed by Giovanni Canali
parent caa67ace93
commit 09596a912c
6 changed files with 71 additions and 36 deletions
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7
tests/test_solver/test_competitive_pinn.py
View File

@@ -24,9 +24,10 @@ inverse_problem = InversePoisson()
inverse_problem.discretise_domain(10)
# reduce the number of data points to speed up testing
data_condition = inverse_problem.conditions["data"]
data_condition.input = data_condition.input[:10]
data_condition.target = data_condition.target[:10]
if hasattr(inverse_problem.conditions, "data"):
data_condition = inverse_problem.conditions["data"]
data_condition.input = data_condition.input[:10]
data_condition.target = data_condition.target[:10]
# add input-output condition to test supervised learning
input_pts = torch.rand(10, len(problem.input_variables))

7
tests/test_solver/test_gradient_pinn.py
View File

@@ -35,9 +35,10 @@ inverse_problem = InversePoisson()
inverse_problem.discretise_domain(10)
# reduce the number of data points to speed up testing
data_condition = inverse_problem.conditions["data"]
data_condition.input = data_condition.input[:10]
data_condition.target = data_condition.target[:10]
if hasattr(inverse_problem.conditions, "data"):
data_condition = inverse_problem.conditions["data"]
data_condition.input = data_condition.input[:10]
data_condition.target = data_condition.target[:10]
# add input-output condition to test supervised learning
input_pts = torch.rand(10, len(problem.input_variables))

7
tests/test_solver/test_pinn.py
View File

@@ -24,9 +24,10 @@ inverse_problem = InversePoisson()
inverse_problem.discretise_domain(10)
# reduce the number of data points to speed up testing
data_condition = inverse_problem.conditions["data"]
data_condition.input = data_condition.input[:10]
data_condition.target = data_condition.target[:10]
if hasattr(inverse_problem.conditions, "data"):
data_condition = inverse_problem.conditions["data"]
data_condition.input = data_condition.input[:10]
data_condition.target = data_condition.target[:10]
# add input-output condition to test supervised learning
input_pts = torch.rand(10, len(problem.input_variables))

7
tests/test_solver/test_rba_pinn.py
View File

@@ -23,9 +23,10 @@ inverse_problem = InversePoisson()
inverse_problem.discretise_domain(10)
# reduce the number of data points to speed up testing
data_condition = inverse_problem.conditions["data"]
data_condition.input = data_condition.input[:10]
data_condition.target = data_condition.target[:10]
if hasattr(inverse_problem.conditions, "data"):
data_condition = inverse_problem.conditions["data"]
data_condition.input = data_condition.input[:10]
data_condition.target = data_condition.target[:10]
# add input-output condition to test supervised learning
input_pts = torch.rand(10, len(problem.input_variables))

7
tests/test_solver/test_self_adaptive_pinn.py
View File

@@ -24,9 +24,10 @@ inverse_problem = InversePoisson()
inverse_problem.discretise_domain(10)
# reduce the number of data points to speed up testing
data_condition = inverse_problem.conditions["data"]
data_condition.input = data_condition.input[:10]
data_condition.target = data_condition.target[:10]
if hasattr(inverse_problem.conditions, "data"):
data_condition = inverse_problem.conditions["data"]
data_condition.input = data_condition.input[:10]
data_condition.target = data_condition.target[:10]
# add input-output condition to test supervised learning
input_pts = torch.rand(10, len(problem.input_variables))