Sensitivity analysis algorithms¶

Warning

Some algorithms may require the installation of GEMSEO with all its features and some others may depend on plugins.

Note

All the features of the wrapped algorithm libraries may not be exposed through GEMSEO.

CorrelationAnalysis¶

Required parameters

disciplines : Collection[MDODiscipline]

The discipline or disciplines to use for the analysis.
n_samples : int

A number of samples. If None, the number of samples is computed by the algorithm.
parameter_space : ParameterSpace

A parameter space.

Optional parameters

algo : str | None, optional

The name of the DOE algorithm. If None, use the SensitivityAnalysis.DEFAULT_DRIVER.

By default it is set to None.
algo_options : Mapping[str, DOELibraryOptionType] | None, optional

The options of the DOE algorithm.

By default it is set to None.
formulation : str, optional

The name of the MDOFormulation to sample the disciplines.

By default it is set to MDF.
output_names : Iterable[str] | None, optional

The disciplines’ outputs to be considered for the analysis. If None, use all the outputs.

By default it is set to None.
**formulation_options : Any

The options of the MDOFormulation.

Required parameters

disciplines : Collection[MDODiscipline]

The discipline or disciplines to use for the analysis.
n_samples : int | None

A number of samples. If None, the number of samples is computed by the algorithm.
parameter_space : ParameterSpace

A parameter space.

Optional parameters

algo : str | None, optional

The name of the DOE algorithm. If None, use the SensitivityAnalysis.DEFAULT_DRIVER.

By default it is set to None.
algo_options : Mapping[str, DOELibraryOptionType] | None, optional

The options of the DOE algorithm.

By default it is set to None.
formulation : str, optional

The name of the MDOFormulation to sample the disciplines.

By default it is set to MDF.
n_replicates : int, optional

The number of times the OAT method is repeated. Used only if n_samples is None. Otherwise, this number is the greater integer \(r\) such that \(r(d+1)\leq\) n_samples and \(r(d+1)\) is the number of samples actually carried out.

By default it is set to 5.
output_names : Iterable[str] | None, optional

The disciplines’ outputs to be considered for the analysis. If None, use all the outputs.

By default it is set to None.
step : float, optional

The finite difference step of the OAT method.

By default it is set to 0.05.
**formulation_options : Any

The options of the MDOFormulation.

Required parameters

disciplines : Collection[MDODiscipline]

The discipline or disciplines to use for the analysis.
n_samples : int

A number of samples. If None, the number of samples is computed by the algorithm.
parameter_space : ParameterSpace

A parameter space.

Optional parameters

algo : str | None, optional

The name of the DOE algorithm. If None, use the SensitivityAnalysis.DEFAULT_DRIVER.

By default it is set to None.
algo_options : Mapping[str, DOELibraryOptionType] | None, optional

The options of the DOE algorithm.

By default it is set to None.
compute_second_order : bool, optional

Whether to compute the second-order indices.

By default it is set to True.
formulation : str, optional

The name of the MDOFormulation to sample the disciplines.

By default it is set to MDF.
output_names : Iterable[str] | None, optional

The disciplines’ outputs to be considered for the analysis. If None, use all the outputs.

By default it is set to None.
use_asymptotic_distributions : bool, optional

Whether to estimate the confidence intervals of the first- and total-order Sobol’ indices with the asymptotic distributions.

By default it is set to True.
**formulation_options : Any

The options of the MDOFormulation.