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WSMSimulateSensitivity

WSMSimulateSensitivity["mmodel",{a,b,}]
simulates "mmodel" and sensitivities to parameters a, b, following experiment settings.

WSMSimulateSensitivity["mmodel",tmax,{a,b,}]
simulates from 0 to tmax.

WSMSimulateSensitivity["mmodel",{tmin,tmax},{a,b,}]
simulates from tmin to tmax.

DetailsDetails

  • WSMSimulateSensitivity returns a WSMSimulationData object.
  • The "mmodel" refers to the fully qualified Modelica name.
  • WSMSimulateSensitivity generates solutions for all variables , as well as derivatives , , for all states , for .
  • Sensitivities can be listed in a WSMSimulationData object sd with sd["SensitivityNames"].
  • The shortest unique model name mmodel can be used where WSMNames["*.mmodel"] gives a unique match.
  • The following options can be given:
  • InterpolationOrderAutomaticcontinuity degree of output between events
    WSMInitialValuesAutomaticoverriding initial values
    WSMInputFunctionsAutomaticoverriding input values
    WSMParameterValuesAutomaticoverriding parameter values
    WSMProgressMonitorAutomaticcontrol display of progress
  • The option setting Automatic normally means that the setting is taken from "mmodel" or its experiment setting.
  • Setting WSMParameterValues or WSMInitialValues to {pi->{c1,c2,},} runs simulations in parallel, with pi taking values cj.
  • WSMInitialValues corresponds to the start property in the Modelica model.
  • WSMInputFunctions->{"var1"->fun1,} uses funi[t] as the input value for vari at time t.
  • The CVODES solver used can be controlled with Method->{"opt1"->val1}.
  • Possible suboptions for the CVODES method include:
  • "InterpolationPoints"Automaticnumber of interpolation points
    "Tolerance"106tolerance for adaptive step size

ExamplesExamplesopen allclose all

Basic Examples  (4)Basic Examples  (4)

Load Wolfram SystemModeler Link:

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Study sensitivity of a parameter over the time interval in model experiment settings:

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Show the sensitivity of a signal to relative changes in a parameter:

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Plot bounds for y and z when varying a by 10%:

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Use the diagram representation of a model as input:

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Copy and paste the output above:

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