# Advanced polynomial chaos construction¶

In this example we are going to expose advanced elements in the construction of a polynomial chaos algorithm:

• construction of the multivariate orthonormal basis,

• truncature strategy of the multivariate orthonormal basis,

• evaluation strategy of the approximation coefficients.

In this example, we consider the following function :

for any .

We assume that the inputs have normal, uniform, gamma and beta distributions :

and , , and are independent.

## Define the model and the input distribution¶

[1]:

from __future__ import print_function
import openturns as ot

[2]:

model = ot.SymbolicFunction(['x1', 'x2', 'x3', 'x4'], ['1+x1*x2 + 2*x3^2+x4^4'])


Create a distribution of dimension 4.

[3]:

distribution = ot.ComposedDistribution([ot.Normal(), ot.Uniform(), ot.Gamma(2.75, 1.0), ot.Beta(2.5, 1.0, -1.0, 2.0)])

[4]:

inputDimension = distribution.getDimension()
inputDimension

[4]:

4


## STEP 1: Construction of the multivariate orthonormal basis¶

Create the univariate polynomial family collection which regroups the polynomial families for each direction.

[5]:

polyColl = ot.PolynomialFamilyCollection(inputDimension)


We could use the Krawtchouk and Charlier families (for discrete distributions).

[6]:

polyColl[0] = ot.KrawtchoukFactory()
polyColl[1] = ot.CharlierFactory()


We could also use the automatic selection of the polynomial which corresponds to the distribution: this is done with the StandardDistributionPolynomialFactory class.

[7]:

for i in range(inputDimension):
marginal = distribution.getMarginal(i)
polyColl[i] = ot.StandardDistributionPolynomialFactory(marginal)


In our specific case, we use specific polynomial factories.

[8]:

polyColl[0] = ot.HermiteFactory()
polyColl[1] = ot.LegendreFactory()
polyColl[2] = ot.LaguerreFactory(2.75)
# Parameter for the Jacobi factory : 'Probabilty' encoded with 1
polyColl[3] = ot.JacobiFactory(2.5, 3.5, 1)


Create the enumeration function.

The first possibility is to use the LinearEnumerateFunction.

[9]:

enumerateFunction = ot.LinearEnumerateFunction(inputDimension)


Another possibility is to use the HyperbolicAnisotropicEnumerateFunction, which gives less weight to interactions.

[10]:

q = 0.4
enumerateFunction_1 = ot.HyperbolicAnisotropicEnumerateFunction(inputDimension, q)


Create the multivariate orthonormal basis which is the the cartesian product of the univariate basis.

[11]:

multivariateBasis = ot.OrthogonalProductPolynomialFactory(polyColl, enumerateFunction)


Ask how many polynomials have total degrees equal to k=5.

[12]:

k = 5
enumerateFunction.getStrataCardinal(k)

[12]:

56


Ask how many polynomials have degrees lower or equal to k=5.

[13]:

enumerateFunction.getStrataCumulatedCardinal(k)

[13]:

126


Give the k-th term of the multivariate basis. To calculate its degree, add the integers.

[14]:

k = 5
enumerateFunction(k)

[14]:


[2,0,0,0]

Build a term of the basis as a NumericalMathFunction. Generally, we do not need to construct manually any term, all terms are constructed automatically by a strategy of construction of the basis.

[15]:

i = 5
Psi_i = multivariateBasis.build(i)
Psi_i

[15]:


-0.707107 + 0.707107 * x0^2

Get the measure mu associated to the multivariate basis.

[16]:

distributionMu = multivariateBasis.getMeasure()
distributionMu

[16]:


ComposedDistribution(Normal(mu = 0, sigma = 1), Uniform(a = -1, b = 1), Gamma(k = 3.75, lambda = 1, gamma = 0), Beta(alpha = 2.5, beta = 1, a = -1, b = 1), IndependentCopula(dimension = 4))

## STEP 2: Truncature strategy of the multivariate orthonormal basis¶

FixedStrategy : all the polynomials af degree lower or equal to 2 which corresponds to the 15 first ones.

[17]:

p = 15
truncatureBasisStrategy = ot.FixedStrategy(multivariateBasis, p)


SequentialStrategy : among the maximumCardinalBasis = 100 first polynomials of the multivariate basis those verfying the convergence criterion.

[18]:

maximumCardinalBasis = 100
truncatureBasisStrategy_1 = ot.SequentialStrategy(
multivariateBasis, maximumCardinalBasis)


CleaningStrategy : among the maximumConsideredTerms = 500 first polynomials, those which have the mostSignificant = 50 most significant contributions with significance criterion significanceFactor equal to The True boolean indicates if we are interested in the online monitoring of the current basis update (removed or added coefficients).

[19]:

maximumConsideredTerms = 500
mostSignificant = 50
significanceFactor = 1.0e-4
truncatureBasisStrategy_2 = ot.CleaningStrategy(
multivariateBasis, maximumConsideredTerms, mostSignificant, significanceFactor, True)


## STEP 3: Evaluation strategy of the approximation coefficients¶

The technique illustrated is the Least Squares technique where the points come from an design of experiments. Here : the Monte Carlo sampling technique.

[20]:

sampleSize = 100
evaluationCoeffStrategy = ot.LeastSquaresStrategy(
ot.MonteCarloExperiment(sampleSize))


You can specify the approximation algorithm. This is the algorithm that generates a sequence of basis using Least Angle Regression.

[21]:

basisSequenceFactory = ot.LARS()


This algorithm estimates the empirical error on each sub-basis using Leave One Out strategy.

[22]:

fittingAlgorithm = ot.CorrectedLeaveOneOut()
# Finally the metamodel selection algorithm embbeded in LeastSquaresStrategy
approximationAlgorithm = ot.LeastSquaresMetaModelSelectionFactory(
basisSequenceFactory, fittingAlgorithm)
evaluationCoeffStrategy_2 = ot.LeastSquaresStrategy(
ot.MonteCarloExperiment(sampleSize), approximationAlgorithm)


Try integration.

[23]:

marginalDegrees = [2] * inputDimension
evaluationCoeffStrategy_3 = ot.IntegrationStrategy(
ot.GaussProductExperiment(distributionMu, marginalDegrees))


## STEP 4: Creation of the Functional Chaos Algorithm¶

The FunctionalChaosAlgorithm class combines

• the model : model

• the distribution of the input random vector : distribution

• the truncature strategy of the multivariate basis

• and the evaluation strategy of the coefficients

[24]:

polynomialChaosAlgorithm = ot.FunctionalChaosAlgorithm(
model, distribution, truncatureBasisStrategy, evaluationCoeffStrategy)