MonteCarloSampler

MonteCarloSampler samples the provided distribution using a traditional Monte Carlo sampling.

Example Input Syntax

[Samplers]
  [./sample]
    type = MonteCarloSampler
    n_samples = 10
    distributions = 'uniform'
    execute_on = 'initial timestep_end'
  [../]
[]
(modules/stochastic_tools/test/tests/samplers/monte_carlo/monte_carlo_uniform.i)

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