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

Methods

  • 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

Distributors

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

This website uses cookies

In order to run our website to your advantage, we use cookies. By using our website you consent to all cookies in accordance with our Cookie Policy. Further information on cookies can be found in our