The calibration aims at optimizing the unknown input parameters of a model from measurement or observation data (inputs and outputs). To perform a calibration, it is first necessary to import a data range (text file) featuring at least one input and one output. The objective is then to calibrate the other inputs and outputs on this model.

There are two types of methods to perform this calibration, each one hypothesizes a linearity or non-linearity of the model:

• Least squares method (linear or non-linear)
• Bayesian method with Gaussian hypothesis (linear or non-linear)

For more information on the calibration method, refer to the following video: