LinearSum

LinearSum function evaluating the model goodness against experiments.

Overview

The LinearSum likelihood function considering $var element = document.getElementById("moose-equation-8ba660e3-673c-4f04-9318-7a6f74161411");katex.render("N", element, {displayMode:false,throwOnError:false});$ experimental configurations is given by:

$(1)var element = document.getElementById("moose-equation-8e14e1ef-3ad1-4e21-926b-d082cb0d2a73");katex.render(" \\mathcal{L} = \\sum_{i=1}^N \\Big(\\frac{\\hat{M}(\\pmb{\\theta},~\\pmb{\\Theta}_i) - M(\\pmb{\\Theta}_i)}{\\sigma}\\Big)^2", element, {displayMode:true,throwOnError:false});$

where, $var element = document.getElementById("moose-equation-5b643045-2fb4-42c5-b308-4b9abe1aa185");katex.render("\\hat{M}(\\pmb{\\theta},~\\pmb{\\Theta}_i)", element, {displayMode:false,throwOnError:false});$ is the model prediction given model parameters $var element = document.getElementById("moose-equation-18128c2b-b03f-4c36-8a46-81fd449cf68b");katex.render("\\pmb{\\theta}", element, {displayMode:false,throwOnError:false});$ and the $var element = document.getElementById("moose-equation-c58b3772-60e6-4c10-9551-7a02182c4451");katex.render("i^{\\text{th}}", element, {displayMode:false,throwOnError:false});$ experimental configuration $var element = document.getElementById("moose-equation-472b07d4-2913-44c3-a0f9-144c732e50ed");katex.render("\\pmb{\\Theta}_i", element, {displayMode:false,throwOnError:false});$ and $var element = document.getElementById("moose-equation-934eaa75-aa5e-48a5-99a1-1d92cc07b971");katex.render("M(\\pmb{\\Theta}_i)", element, {displayMode:false,throwOnError:false});$ is the $var element = document.getElementById("moose-equation-db1d3ce1-169f-48f3-bb59-acf5a3eacd03");katex.render("i^{\\text{th}}", element, {displayMode:false,throwOnError:false});$ experimental data point. $var element = document.getElementById("moose-equation-62c6dd97-73a3-4058-8221-b9c7dcf0e7ae");katex.render("\\sigma", element, {displayMode:false,throwOnError:false});$ above is the scale of the distribution representing the model inadequacy and experimental noise uncertainties.

Example Input File Syntax

[Likelihood<<<{"href": "../../syntax/Likelihood/index.html"}>>>]
  [gaussian]
    type = Gaussian<<<{"description": "Gaussian likelihood function evaluating the model goodness against experiments.", "href": "Gaussian.html"}>>>
    noise<<<{"description": "Experimental noise plus model deviations against experiments."}>>> = 'noise_specified/noise_specified'
    file_name<<<{"description": "Name of the CSV file with experimental values."}>>> = 'exp1.csv'
    log_likelihood<<<{"description": "Compute log-likelihood or likelihood."}>>> = true
  []
[]

Input Parameters

control_tagsAdds user-defined labels for accessing object parameters via control logic.
C++ Type:std::vector<std::string>
Controllable:No
Description:Adds user-defined labels for accessing object parameters via control logic.
enableTrueSet the enabled status of the MooseObject.
Default:True
C++ Type:bool
Controllable:No
Description:Set the enabled status of the MooseObject.

(moose/modules/stochastic_tools/test/tests/likelihoods/gaussian_derived/main.i)

[StochasticTools]
[]

[Distributions]
  [mu1]
    type = Normal
    mean = 0.0
    standard_deviation = 0.5
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    distributions = 'mu1 mu1'
    num_rows = 3
    seed = 2547
  []
[]

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

[Transfers]
  [param]
    type = SamplerParameterTransfer
    to_multi_app = sub
    sampler = sample
    parameters = 'BCs/left/value BCs/right/value'
    # to_control = 'stochastic'
  []
  [reporter_transfer]
    type = SamplerReporterTransfer
    from_reporter = 'average/value'
    stochastic_reporter = 'constant'
    from_multi_app = sub
    sampler = sample
  []
[]

[Reporters]
  [constant]
    type = StochasticReporter
  []
  [noise_specified]
    type = ConstantReporter
    real_names = 'noise_specified'
    real_values = '0.2'
  []
  [likelihoodtest]
    type = TestLikelihood
    likelihoods = 'gaussian'
    model_pred = constant/reporter_transfer:average:value
    sampler = sample
  []
[]

[Likelihood]
  [gaussian]
    type = Gaussian
    noise = 'noise_specified/noise_specified'
    file_name = 'exp1.csv'
    log_likelihood=true
  []
[]

[Executioner]
  type = Steady
[]

[Outputs]
  file_base ='loglikelihood_scalar'
  [out]
    type = JSON
    execute_system_information_on = NONE
  []
[]

Overview
Example Input File Syntax
Input Parameters