[ot-users] Orthogonal basis of a polynomial chaos

regis lebrun regis_anne.lebrun_dutfoy at yahoo.fr
Wed Apr 19 14:32:12 CEST 2017


Hi Michael,


First of all, thanks for using OpenTURNS ;-)! And a remark: in the case of uniform distributions, you are more likely to get Legendre polynomials than Hermite polynomials :-)

Something in your request is not clear for me. What kind of confirmation do you want to get from openTURNS? A string containing the name of the univariate families of polynomials which have been used? The expression of these polynomials in the canonical basis in order to check that they match your expectation? And to get this information, you want to rely only on the algorithm? If yes, is it before or after it has computed the approximation?

Remember that the algorithm can work with ANY multivariate bases, not only polynomial ones, not even tensorized one, so if you rely only on the algorithm you will have to work a little bit to get the information you are looking for (and I will help you if you answer the few questions above).

In your script, you name your MonteCarloeExperiment as 'sample', which is misleading as it is NOT a sample, but rather a way to generate one. It is a particular case of WeightedExperiment, ie an algorithm able to produce samples.

Cheers

Régis


________________________________
De : BAUDIN Michael <michael.baudin at edf.fr>
À : "users at openturns.org" <users at openturns.org> 
Envoyé le : Mercredi 19 avril 2017 13h20
Objet : Re: [ot-users] Orthogonal basis of a polynomial chaos



Hi again,
Here is a script in attachment to experiment with the objects.
Regards,
Michaël
 
De :BAUDIN Michael 
Envoyé : mercredi 19 avril 2017 11:50
À : 'users at openturns.org'
Objet : Orthogonal basis of a polynomial chaos
 
Hi,
 
After a (functionnal) polynomial chaos has been run, I am searching a way to explore what basis was used. For example, if a Uniform distribution was used, I would like to confirm that a Hermite polynomial was used. Of course, this cannot always been done, especially if one of the input distributions was « non-standard ». 
 
However, even in the classical case, I was not able to find out what method of the polynomialChaosAlgorithm could be used to retrieve the information. For example :
 
[…]
# Create Polynomial Chaos
polynomialChaosAlgorithm = FunctionalChaosAlgorithm(myFunction, \
    inputDistribution, fixedStrategy, evalStrategy)
# Compute expansion coefficients
polynomialChaosAlgorithm.run()
 
# Explore the result
myAdapStrat = polynomialChaosAlgorithm.getAdaptiveStrategy() # a AdaptiveStrategy
mybasis = myAdapStrat.getBasis() # a OrthogonalBasis
# and after … ?
 
Afterwhile, I thought that this was not possible, because the basis was an hidden internal object. This is why I tried a second solution : directly explore the polynomial collection used to create the collection of univariate polynomials :
 
# Create a coolection of standard univariate polynomials
polyColl = PolynomialFamilyCollection(dim)
for i in range(dim):
    marginali=inputDistribution.getMarginal(i)
    polyColl[i] = StandardDistributionPolynomialFactory(marginali)
 
# Explore the first polynomial
poly0 = polyColl[0]
# and after … ?
 
But I was not able to find a way to the Hermite polynomial neither.
 
Has anyone an idea ?
 
Regards,
 
Michaël

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