Model Calibration

An automated model calibration efficiently identifies relevant or non-measurable parameters to achieve the best possible match between simulation results and test data. Sensitivity analyses play a decisive role for the prognosis quality of the simulation models.

Best Practice

  • Sensitivity analysis to check unknown parameters for significant influence on the model response
  • CoP supports the identification of the best possible response extraction by comparing model and measured values
  • CoP verifies the uniqueness of the best possible correlation model between parameter and response variation
  • Identification of non-unique (multiple) parameter sets by coupling of parameters


  • Consideration of scalar response values
  • Definition of multi-channel signals, e.g. time-displacement curves
  • Extensive library of functions, e.g. local values as maximum and minimum amplitudes, global values as integrals of certain properties and more complex signal calculations
  • Definition of individual objective functions
  • MOP based sensitivity analysis of different signal properties and pre-evaluation
  • Several optimization algorithms (e.g. gradient-based or nature-inspired)

Postprocessing & Visualization

  • Illustration of statistical evaluations
  • Visualization of signal functions and the corresponding reference value for each design
  • Sensitivities and approximation of signal function values/parameter sensitivities
  • Interactive evaluation of curve fitting and corresponding design images
  • Parallel coordinates plot and cluster analyses for the evaluation of uniqueness


optiSLang is sold worldwide. A list of our distributors can be found here.

Your contact person

Dr.-Ing. Johannes Will

Fon: +49 (0) 3643 9008-30
Fax: +49 (0) 3643 9008-39