A sensitivity analysis studies the "sensitivity" of the outputs of a system to changes in the parameters or initial conditions. It also allows you to rank the model components according to how much they influence the model output. Finally, a sensitivity analysis can be used to identify which model components can be estimated based on a given set of measurements.

Why you will like this module:

• Sensitivity functions: Shows the dynamic sensitivity of your model variables to the model parameters or initial conditions.
• Ranking: Quickly spot the parameters or initial conditions that truely influence your model.
• Identifiability analysis: In one glance, find out which parameters or initial conditions can be estimated for a given set of measurements. You can even do the analysis before any data is collected.

Sensitivity functions

The PhytoSim Sensitivity module performs a local sensitivity analysis. The analysis is called 'local' because it is performed for a model with a given set of parameter values and initial conditions. For that specific reference situation, the Sensitivity module calculates how much the target components change when a source component is changed by only a small amount.

More details in the Sensitivity Analysis User Guide.

Ranking

Analysing individual sensitivity functions can be a difficult task, especially when a large number of source and target components is involved. Therefore, the PhytoSim Sensitivity module also calculates a source component importance ranking, based on the so called sensitivity index, for each of the target components separately and a combined ranking for all target components.

More details in the Sensitivity Analysis User Guide.

Identifiability analysis

A (practical) identifiability analysis is a powerful technique to determine which source components can be calibrated based on data gathered from an experiment, even before the experiment is performed! The experiment is defined by 3 characteristics: (1) which measurements will be performed, (2) what is the measurement accuracy and (3) what is the measurement interval. Based on these experimental degrees of freedom a "virtual" experiment is performed and the so-called collinearity index calculated. This collinearity index is a measure for the linear interdependence of the model parameters and serves as the basis for determining which parameter combinations will be identifiable once experimental data is available.

More details in the Sensitivity Analysis User Guide.

Dependencies (modules required by this module):

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