sensitivity.sensitivity_sobol

sensitivity.sensitivity_sobol

Global sensitivity analysis using Sobol indices.

This module provides routines for variance-based global sensitivity analysis using Sobol indices. Sobol analysis decomposes the variance of model outputs into contributions from individual parameters and their interactions.

The following indices are computed:

• First-order indices (S1)
• Total-effect indices (ST)
• Associated confidence intervals

Sampling is based on Saltelli’s extension of the Sobol sequence and requires (2D + 2) * N model evaluations for D parameters.

References:

- Sobol, I. M. (2001). Math. Comput. Simul., 55, 271–280.
- Saltelli, A. (2002). Comput. Phys. Commun., 145, 280–297.
- Saltelli et al. (2010). Comput. Phys. Commun., 181, 259–270.

Classes

Name	Description
SobolSensitivityAnalysis	Global sensitivity analysis based on Sobol method.

SobolSensitivityAnalysis

sensitivity.sensitivity_sobol.SobolSensitivityAnalysis(
    sensitivity_simulation,
    parameters,
    groups,
    results_path,
    N,
    seed=None,
    n_cores=None,
    cache_results=False,
    **kwargs,
)

Global sensitivity analysis based on Sobol method.

Methods

Name	Description
calculate_sensitivity	Calculate the sensitivity matrices for SOBOL analysis.
create_samples	Create samples for sobol.

calculate_sensitivity

sensitivity.sensitivity_sobol.SobolSensitivityAnalysis.calculate_sensitivity(
    cache_filename=None,
    cache=False,
)

Calculate the sensitivity matrices for SOBOL analysis.

create_samples

sensitivity.sensitivity_sobol.SobolSensitivityAnalysis.create_samples()

Create samples for sobol.

Generates model inputs using Saltelli’s extension of the Sobol’ sequence

The Sobol’ sequence is a popular quasi-random low-discrepancy sequence used to generate uniform samples of parameter space.