The Calibration module can be used to automatically fit a model to measured data. Calibration is also sometimes called parameterisation or parameter estimation. An optimization algorithm is used in order to minimize the difference between the model simulations and the measured data by changing model parameter or derived variable initial values. The Calibration module will use the current simulation as configured in the Simulation module to perform the model evaluations.

Additionally, a confidence information calculation can also be performed giving you an indication of the error of the estimated values and the correlations between the optimizer variables.

Why you will like this module:

• Automatic calibration: If you recognize following situation, then this module is for you.

  - Let's change this parameter...

  - Ok, now we do a simulation...

  - Let's have a look at the results...

  - Mmm, not quite there yet, let's change the parameter a bit more...

  - Running another simulation...

  - Oh oh, that made it worse...

  - Mmm, maybe I'll change another parameter...

  - ...

• Confidence information: Obtain estimation errors and the correlation matrix.
• Compilation free modelling: Immediately start calibrating a changed model.
• Automatic unit conversions: Differences between model and calibration data units are automatically resolved.
• Calibration progress visualization: (suspense guaranteed :-))
  • See how your estimated parameters and/or initial conditions evolve during the optimization.
  • Watch how the model simulations get closer to the measured data.
  • Follow the objective value while it creeps closer to zero.

Optimizer Variables

Optimizer variables are model quantities (parameters or derived variable initial conditions) that the optimization algorithm is allowed to change in order to minimize the difference between measurements and simulation. They are allowed to take continuous or discrete values bounded by a minimum and maximum value.

More details in the Calibration User Guide.

Objective Variables

Objective variables are model components for which measured data is available and which will be used to calibrate the model. The Calibration module uses a weighted sum of squared errors objective value to minimize the difference between the measured data and the simulated values.

More details in the Calibration User Guide.

Confidence Information

A confidence information calculation can be performed in order to get an indication of the error of the estimated values and the parameter correlations. The cornerstone of the confidence information is the estimation covariance matrix which is calculated using a nested Richardson extrapolation finite difference approach. Based on the covariance matrix, relative standard errors and the correlation matrix are calculated.

More details in the Calibration User Guide.

Built-in optimizers:

• Simplex (local search): Also known as the Nelder-Mead method or downhill simplex method or amoeba method. For more information see: http://en.wikipedia.org/wiki/Nelder-Mead_method.


• Grid search (global search): Based on the optimizer variable bounds and the number of required intervals a search grid is constructed. At each point of the grid, a model evaluation is performed and the objective evaluated. Strickly speaking this is not a true optimization but it can be useful as a first exploration of the search space.


• Single shot (single evaluation): The objective is evaluated once at the initial values of the optimizer variables.

Dependencies (modules required by this module):

• PhytoSim Data I/O
• PhytoSim Modelling
• PhytoSim Simulation