Use the Adaptive Directional Stratification Algorithm

In this example we estimate a failure probability with the adaptive directional simulation algorithm provided by the AdaptiveDirectionalStratification class.


The adaptive directional simulation algorithm operates in the standard. It relies on:

  1. a root strategy to evaluate the nearest failure point along each direction and take the contribution of each direction to the failure event probability into account. The available strategies are: - RiskyAndFast - MediumSafe - SafeAndSlow

  2. a sampling strategy to choose directions in the standard space. The available strategies are: - RandomDirection - OrthogonalDirection

Let us consider the analytical example of the cantilever beam described here.

from openturns.usecases import cantilever_beam
import openturns as ot


We load the model from the usecases module :

cb = cantilever_beam.CantileverBeam()

We load the joint probability distribution of the input parameters :

distribution = cb.distribution

We load the model giving the displacement at the end of the beam :

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(), 0.30)

Root finding algorithm.

solver = ot.Brent()
rootStrategy = ot.MediumSafe(solver)

Direction sampling algorithm.

samplingStrategy = ot.RandomDirection()

Create a simulation algorithm.

algo = ot.AdaptiveDirectionalStratification(event, rootStrategy, samplingStrategy)

Retrieve results.

result = algo.getResult()
probability = result.getProbabilityEstimate()
print("Pf=", probability)
print("Iterations=", result.getOuterSampling())
probabilityEstimate=4.847378e-07 varianceEstimate=1.286438e-15 standard deviation=3.59e-08 coefficient of variation=7.40e-02 confidenceLength(0.95)=1.41e-07 outerSampling=39995 blockSize=1
Pf= 4.847378114652009e-07
Iterations= 39995

Total running time of the script: (0 minutes 2.010 seconds)