SobolSampler

The SobolSampler object generates the necessary matrices of Monte Carlo samples to perform a variance-based sensitivity analysis, refer to Saltelli (2002) for complete details.

Input Parameters

  • distributionsThe names of distributions that you want to sample.

    C++ Type:std::vector

    Options:

    Description:The names of distributions that you want to sample.

  • n_samplesNumber of Monte Carlo samples to perform for each distribution.

    C++ Type:unsigned int

    Options:

    Description:Number of Monte Carlo samples to perform for each distribution.

Required Parameters

  • execute_onLINEARThe list of flag(s) indicating when this object should be executed, the available options include NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, CUSTOM.

    Default:LINEAR

    C++ Type:ExecFlagEnum

    Options:NONE INITIAL LINEAR NONLINEAR TIMESTEP_END TIMESTEP_BEGIN FINAL CUSTOM

    Description:The list of flag(s) indicating when this object should be executed, the available options include NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, CUSTOM.

  • seed0Random number generator initial seed

    Default:0

    C++ Type:unsigned int

    Options:

    Description:Random number generator initial seed

Optional Parameters

  • control_tagsAdds user-defined labels for accessing object parameters via control logic.

    C++ Type:std::vector

    Options:

    Description:Adds user-defined labels for accessing object parameters via control logic.

  • enableTrueSet the enabled status of the MooseObject.

    Default:True

    C++ Type:bool

    Options:

    Description:Set the enabled status of the MooseObject.

Advanced Parameters

Input Files

References

  1. Andrea Saltelli. Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications, 145(2):280–297, 2002. URL: https://doi.org/10.1016/S0010-4655(02)00280-1.[BibTeX]