analyze_sensitivity_polynomial_chaos

pyapprox.sensitivity_analysis.analyze_sensitivity_polynomial_chaos(pce, max_order=2)

Compute variance based sensitivity metrics from a polynomial chaos expansion

Parameters

pce :class:`pyapprox.multivariate_polynomials.PolynomialChaosExpansion`

The polynomial chaos expansion

max_orderinteger

The maximum interaction order of Sonol indices to compute. A value of 2 will compute all pairwise interactions, a value of 3 will compute indices for all interactions involving 3 variables. The number of indices returned will be nchoosek(nvars+max_order,nvars). Warning when nvars is high the number of indices will increase rapidly with max_order.

Returns

resultpyapprox.sensitivity_analysis.SensivitityResult

Result object with the following attributes

main_effectsnp.ndarray (nvars)

The variance based main effect sensitivity indices

total_effectsnp.ndarray (nvars)

The variance based total effect sensitivity indices

sobol_indicesnp.ndarray (nchoosek(nvars+max_order,nvars),nqoi)

The variance based Sobol sensitivity indices

sobol_interaction_indicesnp.ndarray(nvars,nchoosek(nvars+max_order,nvars))

Indices specifying the variables in each interaction in sobol_indices