June 1-3, 2015 in Dolní Morava, Czech Republic

Conference for numerical simulation

Jun 6-8, 2016 in Würzburg

Conference for technical aspects in the design and use of modern connectors

Jun 7-8, 2016 in Munich

Computer-aided engineering conference in the automotive industry.

Jun 12-15, 2016 in Linz

Challenges in Forming High-Strength Sheets

Jun 6, 2016 in Winterthur

Conference for numerical simulation


Library RDO Journal Weimar Optimization and Stochastic Days Newsletter Quarter 2_2016 Dynardo...

Dynardo GmbH is proud to announce that Dynardo’s optiSLang, leading edge software for CAE-based...

Release note optiSLang 5.0.0 more information about optiSLang 5 more information about optiSLang...



Jun 6-8, 2016 in Weimar

Jun 9 in Vienna
Aug 10, 2016 in Lein.-Echt.
Sep 7, 2016 in Renens
Nov 16, 2016 in Berlin
Nov 22, 2016 in Aadorf

Jun 22 in Hanover
Sep 30, 2016 in Lein.-Echt.
Dec 9, 2016 in Grafing

Aug 12, 2016 in Lein.-Echt.
Nov 18, 2016 in Berlin
Nov 24, 2016 in Aadorf

Training: Parameter Identification with optiSLang

This training 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.


Signal data
Model Calibration
Parameter identification sensitivity analysis

1. Basics of model calibration

  • Maximum likelihood method
  • Weighted least squares method
  • Definition of signals and signal functions

2. Application of sensitivity analysis for calibration problems

  • Definition of parameter spaces and design of experiments
  • Metamodel of Optimal Prognosis
  • Exclusion of non-identifiable variables

3. Solving of calibration problems as an optimization task

  • Definition of proper objectives
  • Globale vs. local search

4. Analysis of non-identifiable parameters

  • Analysis of parameter relevance
  • Evaluation of parameter correctness concerning inaccurate measurements
  • Evaluation of parameter scatter from various measurements


The training aims at engineers and developers regularly dealing with the task of model calibration in the field of CAE. Prerequisite is basic knowledge about the command of optiSLang which can be obtained by the training "optiSLang basics".


Dipl.-Ing. Rene Kallmeyer

to be announced
Please register not later than 14 days prior to the event date.

1 Day Seminar from 9am-5pm

min. 5 to max. 12 participants 

500 Euro (excluding VAT)

50% for students
30% for university members / PHD's

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