Note
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Use a randomized QMC algorithmΒΆ
In this example we are going to estimate a failure probability on the cantilever beam.
from openturns.usecases import cantilever_beam
import openturns as ot
ot.Log.Show(ot.Log.NONE)
We load the data class containing the probabilistic modeling of the beam.
cb = cantilever_beam.CantileverBeam()
We load the joint probability distribution of the input parameters :
distribution = cb.distribution
We load the model as well,
model = cb.model
We create the event whose probability we want to estimate.
vect = ot.RandomVector(distribution)
G = ot.CompositeRandomVector(model, vect)
event = ot.ThresholdEvent(G, ot.Greater(), 0.3)
Define the low discrepancy sequence.
sequence = ot.SobolSequence()
Create a simulation algorithm.
experiment = ot.LowDiscrepancyExperiment(sequence, 1)
experiment.setRandomize(True)
algo = ot.ProbabilitySimulationAlgorithm(event, experiment)
algo.setMaximumCoefficientOfVariation(0.05)
algo.setMaximumOuterSampling(int(1e5))
algo.run()
Retrieve results.
result = algo.getResult()
probability = result.getProbabilityEstimate()
print("Pf=", probability)
Pf= 0.0