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 distribution names to be sampled, the number of distributions provided defines the number of columns per matrix.
C++ Type:std::vector
Options:
Description:The distribution names to be sampled, the number of distributions provided defines the number of columns per matrix.
num_rowsThe number of rows per matrix to generate.
C++ Type:unsigned int
Options:
Description:The number of rows per matrix to generate.

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 PRE_MULTIAPP_SETUP
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.
legacy_supportTrueDisables errors for legacy API support.
Default:True
C++ Type:bool
Options:
Description:Disables errors for legacy API support.
limit_get_global_samples429496729The maximum allowed number of items in the DenseMatrix returned by getGlobalSamples method.
Default:429496729
C++ Type:unsigned int
Options:
Description:The maximum allowed number of items in the DenseMatrix returned by getGlobalSamples method.
limit_get_local_samples429496729The maximum allowed number of items in the DenseMatrix returned by getLocalSamples method.
Default:429496729
C++ Type:unsigned int
Options:
Description:The maximum allowed number of items in the DenseMatrix returned by getLocalSamples method.
limit_get_next_local_row429496729The maximum allowed number of items in the std::vector returned by getNextLocalRow method.
Default:429496729
C++ Type:unsigned int
Options:
Description:The maximum allowed number of items in the std::vector returned by getNextLocalRow method.
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

modules/stochastic_tools/test/tests/transfers/sobol/sobol.i
modules/stochastic_tools/test/tests/samplers/sobol/sobol.i
modules/stochastic_tools/test/tests/transfers/sampler_postprocessor/errors/require_stochastic_results.i
modules/stochastic_tools/test/tests/transfers/sampler_postprocessor/master.i
modules/stochastic_tools/test/tests/transfers/sampler_transfer/sobol.i
modules/stochastic_tools/test/tests/vectorpostprocessors/multiple_stochastic_results/master.i
modules/stochastic_tools/test/tests/vectorpostprocessors/stochastic_results/master.i
modules/stochastic_tools/test/tests/transfers/sampler_postprocessor/errors/wrong_multi_app.i

modules/stochastic_tools/test/tests/transfers/sobol/sobol.i

[Mesh]
  type = GeneratedMesh
  dim = 1
  nx = 1
  ny = 1
[]

[Variables]
  [u]
  []
[]

[Distributions]
  [uniform_left]
    type = UniformDistribution
    lower_bound = 0
    upper_bound = 0.5
  []
  [uniform_right]
    type = UniformDistribution
    lower_bound = 1
    upper_bound = 2
  []
[]

[Samplers]
  [sample]
    type = SobolSampler
    num_rows = 3
    distributions = 'uniform_left uniform_right'
    execute_on = INITIAL # create random numbers on initial and use them for each timestep
  []
[]

[MultiApps]
  [sub]
    type = SamplerTransientMultiApp
    input_files = sub.i
    sampler = sample
  []
[]

[Transfers]
  [sub]
    type = SamplerParameterTransfer
    multi_app = sub
    sampler = sample
    parameters = 'BCs/left/value BCs/right/value'
    to_control = 'stochastic'
    execute_on = INITIAL
    check_multiapp_execute_on = false
  []
[]

[Executioner]
  type = Transient
  num_steps = 5
  dt = 0.01
[]

[Problem]
  solve = false
  kernel_coverage_check = false
[]

[Outputs]
  execute_on = 'INITIAL TIMESTEP_END'
[]

modules/stochastic_tools/test/tests/samplers/sobol/sobol.i

[Mesh]
  type = GeneratedMesh
  dim = 1
  nx = 1
  ny = 1
[]

[Variables]
  [u]
  []
[]

[Distributions]
  [d0]
    type = UniformDistribution
    lower_bound = 0
    upper_bound = 1
  []
  [d1]
    type = UniformDistribution
    lower_bound = 10
    upper_bound = 11
  []
  [d2]
    type = UniformDistribution
    lower_bound = 100
    upper_bound = 101
  []
[]

[Samplers]
  [sample]
    type = SobolSampler
    num_rows = 4
    distributions = 'd0 d1 d2'
    execute_on = 'initial'
  []
[]

[VectorPostprocessors]
  [data]
    type = SamplerData
    sampler = sample
    execute_on = 'initial'
  []
[]

[Executioner]
  type = Steady
[]

[Problem]
  solve = false
  kernel_coverage_check = false
[]

[Outputs]
  execute_on = 'INITIAL'
  csv = true
[]

modules/stochastic_tools/test/tests/transfers/sampler_postprocessor/errors/require_stochastic_results.i

[Mesh]
  type = GeneratedMesh
  dim = 1
  nx = 1
  ny = 1
[]

[Variables]
  [u]
  []
[]

[Distributions]
  [uniform_left]
    type = UniformDistribution
    lower_bound = 0
    upper_bound = 0.5
  []
  [uniform_right]
    type = UniformDistribution
    lower_bound = 1
    upper_bound = 2
  []
[]

[Samplers]
  [sample]
    type = SobolSampler
    num_rows = 3
    distributions = 'uniform_left uniform_right'
    execute_on = INITIAL # create random numbers on initial and use them for each timestep
  []
[]

[MultiApps]
  [sub]
    type = SamplerTransientMultiApp
    input_files = sub.i
    sampler = sample
  []
[]

[Transfers]
  [runner]
    type = SamplerParameterTransfer
    multi_app = sub
    sampler = sample
    parameters = 'BCs/left/value BCs/right/value'
    to_control = 'stochastic'
    execute_on = INITIAL
    check_multiapp_execute_on = false
  []
  [data]
    type = SamplerPostprocessorTransfer
    multi_app = sub
    sampler = sample
    to_vector_postprocessor = storage
    from_postprocessor = avg
    execute_on = timestep_end
    check_multiapp_execute_on = false
  []
[]

[VectorPostprocessors]
  [storage]
    type = ConstantVectorPostprocessor
    value = 0
  []
[]

[Executioner]
  type = Transient
  num_steps = 5
  dt = 0.01
[]

[Problem]
  solve = false
  kernel_coverage_check = false
[]

[Outputs]
  csv = true
[]

modules/stochastic_tools/test/tests/transfers/sampler_postprocessor/master.i

[Mesh]
  type = GeneratedMesh
  dim = 1
  nx = 1
  ny = 1
[]

[Variables]
  [u]
  []
[]

[Distributions]
  [uniform_left]
    type = UniformDistribution
    lower_bound = 0
    upper_bound = 0.5
  []
  [uniform_right]
    type = UniformDistribution
    lower_bound = 1
    upper_bound = 2
  []
[]

[Samplers]
  [sample]
    type = SobolSampler
    num_rows = 3
    distributions = 'uniform_left uniform_right'
    execute_on = INITIAL # create random numbers on initial and use them for each timestep
  []
[]

[MultiApps]
  [sub]
    type = SamplerTransientMultiApp
    input_files = sub.i
    sampler = sample
    execute_on = 'INITIAL TIMESTEP_BEGIN'
  []
[]

[Transfers]
  [runner]
    type = SamplerParameterTransfer
    multi_app = sub
    sampler = sample
    parameters = 'BCs/left/value BCs/right/value'
    to_control = 'stochastic'
    execute_on = INITIAL
    check_multiapp_execute_on = false
  []
  [data]
    type = SamplerPostprocessorTransfer
    multi_app = sub
    sampler = sample
    to_vector_postprocessor = storage
    from_postprocessor = avg
    execute_on = TIMESTEP_BEGIN
    check_multiapp_execute_on = false
  []
[]

[VectorPostprocessors]
  [storage]
    type = StochasticResults
    samplers = sample
    execute_on = 'INITIAL TIMESTEP_END'
  []
[]

[Executioner]
  type = Transient
  num_steps = 5
  dt = 0.01
[]

[Problem]
  solve = false
  kernel_coverage_check = false
[]

[Outputs]
  csv = true
[]

modules/stochastic_tools/test/tests/transfers/sampler_transfer/sobol.i

[Mesh]
  type = GeneratedMesh
  dim = 1
  nx = 1
  ny = 1
[]

[Variables]
  [u]
  []
[]

[Distributions]
  [uniform_left]
    type = UniformDistribution
    lower_bound = 0
    upper_bound = 0.5
  []
  [uniform_right]
    type = UniformDistribution
    lower_bound = 1
    upper_bound = 2
  []
[]

[Samplers]
  [sample]
    type = SobolSampler
    num_rows = 3
    distributions = 'uniform_left uniform_right'
    execute_on = INITIAL # create random numbers on initial and use them for each timestep
  []
[]

[MultiApps]
  [sub]
    type = SamplerTransientMultiApp
    input_files = sub.i
    sampler = sample
  []
[]

[Transfers]
  [sub]
    type = SamplerParameterTransfer
    multi_app = sub
    sampler = sample
    parameters = 'BCs/left/value BCs/right/value'
    to_control = 'stochastic'
    execute_on = INITIAL
    check_multiapp_execute_on = false
  []
[]

[Executioner]
  type = Transient
  num_steps = 5
  dt = 0.01
[]

[Problem]
  solve = false
  kernel_coverage_check = false
[]

[Outputs]
  execute_on = 'INITIAL TIMESTEP_END'
[]

modules/stochastic_tools/test/tests/vectorpostprocessors/multiple_stochastic_results/master.i

[Mesh]
  type = GeneratedMesh
  dim = 1
  nx = 1
  ny = 1
[]

[Variables]
  [u]
  []
[]

[Distributions]
  [uniform_left]
    type = UniformDistribution
    lower_bound = 0
    upper_bound = 0.5
  []
  [uniform_right]
    type = UniformDistribution
    lower_bound = 1
    upper_bound = 2
  []
[]

[Samplers]
  [sobol]
    type = SobolSampler
    num_rows = 3
    distributions = 'uniform_left uniform_right'
    execute_on = INITIAL # create random numbers on initial and use them for each timestep
  []
  [mc]
    type = MonteCarloSampler
    num_rows = 5
    distributions = 'uniform_left uniform_right'
    execute_on = INITIAL # create random numbers on initial and use them for each timestep
  []
[]

[MultiApps]
  [sobol]
    type = SamplerTransientMultiApp
    input_files = sub.i
    sampler = sobol
  []
  [mc]
    type = SamplerTransientMultiApp
    input_files = sub.i
    sampler = mc
  []
[]

[Transfers]
  [sobol]
    type = SamplerParameterTransfer
    multi_app = sobol
    sampler = sobol
    parameters = 'BCs/left/value BCs/right/value'
    to_control = 'stochastic'
  []
  [sobol_data]
    type = SamplerPostprocessorTransfer
    multi_app = sobol
    sampler = sobol
    to_vector_postprocessor = storage
    from_postprocessor = avg
  []

  [mc]
    type = SamplerParameterTransfer
    multi_app = mc
    sampler = mc
    parameters = 'BCs/left/value BCs/right/value'
    to_control = 'stochastic'
  []
  [mc_data]
    type = SamplerPostprocessorTransfer
    multi_app = mc
    sampler = mc
    to_vector_postprocessor = storage
    from_postprocessor = avg
  []
[]

[VectorPostprocessors]
  [storage]
    type = StochasticResults
    parallel_type = REPLICATED
    samplers = 'sobol mc'
  []
[]

[Executioner]
  type = Transient
  num_steps = 1
[]

[Problem]
  solve = false
  kernel_coverage_check = false
[]

[Outputs]
  [out]
    type = CSV
    execute_on = FINAL
  []
[]

modules/stochastic_tools/test/tests/vectorpostprocessors/stochastic_results/master.i

[Mesh]
  type = GeneratedMesh
  dim = 1
  nx = 1
  ny = 1
[]

[Variables]
  [u]
  []
[]

[Distributions]
  [uniform_left]
    type = UniformDistribution
    lower_bound = 0
    upper_bound = 0.5
  []
  [uniform_right]
    type = UniformDistribution
    lower_bound = 1
    upper_bound = 2
  []
[]

[Samplers]
  [sample]
    type = SobolSampler
    num_rows = 3
    distributions = 'uniform_left uniform_right'
    execute_on = INITIAL # create random numbers on initial and use them for each timestep
  []
[]

[MultiApps]
  [sub]
    type = SamplerTransientMultiApp
    input_files = sub.i
    sampler = sample
  []
[]

[Transfers]
  [runner]
    type = SamplerParameterTransfer
    multi_app = sub
    sampler = sample
    parameters = 'BCs/left/value BCs/right/value'
    to_control = 'stochastic'
  []
  [data]
    type = SamplerPostprocessorTransfer
    multi_app = sub
    sampler = sample
    to_vector_postprocessor = storage
    from_postprocessor = avg
  []
[]

[VectorPostprocessors]
  [storage]
    type = StochasticResults
    parallel_type = DISTRIBUTED
    samplers = sample
  []
[]

[Executioner]
  type = Transient
  num_steps = 1
[]

[Problem]
  solve = false
  kernel_coverage_check = false
[]

[Outputs]
  [out]
    type = CSV
    execute_on = FINAL
  []
[]

modules/stochastic_tools/test/tests/transfers/sampler_postprocessor/errors/wrong_multi_app.i

[Mesh]
  type = GeneratedMesh
  dim = 1
  nx = 1
  ny = 1
[]

[Variables]
  [u]
  []
[]

[Distributions]
  [uniform_left]
    type = UniformDistribution
    lower_bound = 0
    upper_bound = 0.5
  []
  [uniform_right]
    type = UniformDistribution
    lower_bound = 1
    upper_bound = 2
  []
[]

[Samplers]
  [sample]
    type = SobolSampler
    num_rows = 3
    distributions = 'uniform_left uniform_right'
    execute_on = INITIAL # create random numbers on initial and use them for each timestep
  []
[]

[MultiApps]
  [sub]
    type = TransientMultiApp
    input_files = sub.i
    positions = '0 0 0'
  []
[]

[Transfers]
  [runner]
    type = SamplerParameterTransfer
    multi_app = sub
    sampler = sample
    parameters = 'BCs/left/value BCs/right/value'
    to_control = 'stochastic'
    execute_on = INITIAL
    check_multiapp_execute_on = false
  []
  [data]
    type = SamplerPostprocessorTransfer
    multi_app = sub
    sampler = sample
    to_vector_postprocessor = storage
    from_postprocessor = avg
    execute_on = timestep_end
    check_multiapp_execute_on = false
  []
[]

[VectorPostprocessors]
  [storage]
    type = StochasticResults
    samplers = sample
  []
[]

[Executioner]
  type = Transient
  num_steps = 5
  dt = 0.01
[]

[Problem]
  solve = false
  kernel_coverage_check = false
[]

[Outputs]
  csv = true
[]

References

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.

@article{saltelli2002making,
    author = "Saltelli, Andrea",
    title = "Making best use of model evaluations to compute sensitivity indices",
    journal = "Computer Physics Communications",
    volume = "145",
    number = "2",
    pages = "280--297",
    year = "2002",
    publisher = "Elsevier",
    url = "https://doi.org/10.1016/S0010-4655(02)00280-1"
}

Input Parameters
Input Files
References

Application Usage

Physics and Syntax

Application Development

Framework Development

MOOSEDocs

Infrastructure

Questions

Information and Tools