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Ansys optiSLang Model Calibration and Parameter Identification (Self-paced Learning Available)

Course Overview

This course gives information about the mathematical basis of model calibration. Furthermore, the issue of relevance and quality of the identified parameters will be discussed. These methods can be applied easily for any RDO task with the help of optiSLang. A key role is the definition of signals and signal functions as well as the sensitivity analysis using the Metamodel of Optimal Prognosis (MOP). 
Model calibration means to adapt the results of simulation models to actual measurement data. Here, a measured response curve, e.g., a load displacement curve, is taken as a reference and parameters of the simulation model will be modified until the best correlation between reference and simulation is obtained. This method is also known as "reverse engineering". Using this methodology, parameters that cannot be measured directly, such as material parameters, are identified. Therefore, this method is called parameter identification.


Teaching Method

Lectures and computer practical sessions to validate acquired knowledge.

Learning Outcome

Following completion of this course, you will be able to:

  • Find the best fit between simulation and given measurements
  • Definition of calibration task, signals, and signal functions

Available Dates

Currently, no training dates available

Learning Options

Training materials for this course are available with an Ansys Learning Hub Subscription. If there is no active public schedule available, private training can be arranged. 

Self-paced Learning 

Complete a class on your own schedule at your own pace. Scope is equivalent to Instructor led classes. Includes video lecture, workshops and input files. All our Self-Paced video courses are only available with an Ansys Learning Hub subscription.


This is a 1-day classroom course covering both lectures and workshops. For virtual training, this course is covered over 2 x 2-hour sessions lectures only.

Virtual Classroom Session 1

Basic Idea and theoretical background of Parameter identification and practical usage in the Graphical User Interface.

  • Lecture: Overview and Theoretical background (Least squares minimization)
  • Demonstration: Oscillator Calibration in optiSLang standalone
  • Parametrization of characteristic curves as signals
  • Sensitivity analysis 
  • Definition of objective functions
  • Postprocessing of Results
  • Lecture and Demonstration: Uniqueness of optimized parameter
  • Workshop: Oscillator Calibration inside Workbench

Virtual Classroom Session 2

Parameter identification using Signal responses

  • Objective definition
  • Example: Spring Steel Calibration inside Workbench
  • From Parametrization until results Postprocessing (using Vectors)
  • Further hints (based on a calibration of hyper elasticity parameters of an OGDEN law)
  • Demonstration: Spring Steel Calibration inside Workbench
  • Workshop: Wedge Splitting Calibration in optiSLang standalone
  • System Calibration

With 2021R2-> with Signal MOP

  • Analysis with MOP (scalar) vs. Signal MOP (field)