df673cad4e
* solvers -> solver * adaptive_functions -> adaptive_function * callbacks -> callback * operators -> operator * pinns -> physics_informed_solver * layers -> block
45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
from pina.solver import PINN
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from pina.trainer import Trainer
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from pina.model import FeedForward
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from pina.problem.zoo import Poisson2DSquareProblem as Poisson
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from pina.callback import R3Refinement
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# make the problem
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poisson_problem = Poisson()
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boundaries = ['g1', 'g2', 'g3', 'g4']
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n = 10
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poisson_problem.discretise_domain(n, 'grid', domains=boundaries)
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poisson_problem.discretise_domain(n, 'grid', domains='D')
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model = FeedForward(len(poisson_problem.input_variables),
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len(poisson_problem.output_variables))
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# make the solver
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solver = PINN(problem=poisson_problem, model=model)
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# def test_r3constructor():
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# R3Refinement(sample_every=10)
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# def test_r3refinment_routine():
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# # make the trainer
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# trainer = Trainer(solver=solver,
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# callback=[R3Refinement(sample_every=1)],
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# accelerator='cpu',
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# max_epochs=5)
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# trainer.train()
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# def test_r3refinment_routine():
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# model = FeedForward(len(poisson_problem.input_variables),
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# len(poisson_problem.output_variables))
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# solver = PINN(problem=poisson_problem, model=model)
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# trainer = Trainer(solver=solver,
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# callback=[R3Refinement(sample_every=1)],
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# accelerator='cpu',
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# max_epochs=5)
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# before_n_points = {loc : len(pts) for loc, pts in trainer.solver.problem.input_pts.items()}
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# trainer.train()
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# after_n_points = {loc : len(pts) for loc, pts in trainer.solver.problem.input_pts.items()}
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# assert before_n_points == after_n_points
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