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"""
Use a randomized QMC algorithm
==============================
"""
# %%
# In this example we are going to estimate a failure probability on the :ref:`stressed beam <use-case-stressed-beam>`.
# %%
from openturns.usecases import stressed_beam
import openturns as ot
# %%
# We load the data class containing the probabilistic modeling of the beam.
sm = stressed_beam.AxialStressedBeam()
# %%
# We load the joint probability distribution of the input parameters :
distribution = sm.distribution
# %%
# We load the model as well,
model = sm.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.Less(), 0.0)
# %%
# 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(1e4))
algo.run()
# %%
# Retrieve results.
# %%
result = algo.getResult()
probability = result.getProbabilityEstimate()
print("Pf=", probability)
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