Model Calibration

Model calibrations via Bayesian inference to integrate experiments to improve model performance: In this analysis, set <AnalysisType>model_calibration</AnalysisType>. When a dynamic model is provided, the users need to set <pivot> and <dynamic> node in the <GlobalSettings>. As illustrated in the following example. In addition, the initial values for input variables can be provided via <InitialInputs>.

<?xml version="1.0" ?>
<Simulation verbosity="debug">

  <RunInfo>
    <WorkingDir>calibration_dynamic</WorkingDir>
    <batchSize>1</batchSize>
  </RunInfo>

  <GlobalSettings>
    <AnalysisType>model_calibration</AnalysisType>
    <pivot>time</pivot>
    <dynamic>True</dynamic>
    <limit>100</limit>
    <Inputs>alpha, beta, gamma</Inputs>
    <InitialInputs>0.1, 4.0, -1.0</InitialInputs>
    <Outputs>eta</Outputs>
  </GlobalSettings>

  <LikelihoodModel>
    <simTargets>eta</simTargets>
    <expTargets shape="1,50" computeCov='False' correlation='False'>
      -1.16074224 -1.10303445 -1.02830511 -0.89782965 -0.73765453 -0.7989537
      -0.86163706 -1.02209944 -1.12444044 -1.23657398 -1.16081758 -1.01219869
      -0.890747   -0.80444122 -0.70893668 -0.61012531 -0.65670863 -0.6768583
      -0.74732441 -0.81448647 -0.73232671 -0.54989334 -0.39796749 -0.07894291
        0.13067378  0.28999998  0.27418965  0.313329    0.32306704  0.2885684
        0.32736775  0.52458854  0.69446572  0.82419521  1.04393683  1.00435818
        1.0810376   0.97245373  0.82406522  0.76067559  0.70145544  0.79479965
        0.88035895  0.97750307  1.11524353  1.17159017  1.18299222  1.07255006
        1.02835909  0.90784132
    </expTargets>
    <expCov diag="True">
        0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
        0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
        0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
        0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
        0.02, 0.02, 0.02, 0.02, 0.02, 0.02
    </expCov>
    <!-- <biasTargets></biasTargets>
    <biasCov diag="False"></biasCov> -->
    <!-- <romCov diag="True"></romCov> -->
  </LikelihoodModel>

  <Distributions>
    <Uniform name='alpha'>
      <lowerBound>0.1</lowerBound>
      <upperBound>0.3</upperBound>
    </Uniform>
    <Uniform name='beta'>
      <lowerBound>4</lowerBound>
      <upperBound>6</upperBound>
    </Uniform>
    <Uniform name='gamma'>
      <lowerBound>-1</lowerBound>
      <upperBound>1</upperBound>
    </Uniform>
  </Distributions>

  <Models>
    <ExternalModel ModuleToLoad="../models/model_cal" name="model" subType="">
      <inputs>inputGroup</inputs>
      <outputs>outputGroup</outputs>
    </ExternalModel>
  </Models>

</Simulation>