Sensitivity analysis tries to establish the influence of the input variable on the output of a mathematical model or system (numerical or otherwise). This is a crucial aspect when the sensitivity of a system to uncertain geometry, material properties, manufacturing tolerances, etc. needs to be determined. Based on the results of the sensitivity analysis, inputs important for the design can be focused on.

The sensitivity analysis is performed with the Monte Carlo analysis applied directly to the final model and each increment function independently. The sensitivity of variable takes into account all increment functions, which belongs to the given variable. For example, variable X2 considers increment functions which have number ’2’ in their description, e.g. dF2, dF1.2, dF2.4.5 etc…, and these increment functions influence the domain of variable X2. This type of sensitivity analysis is similar to commonly used sensitivity analysis. However, there is a difference between variable and increment sensitivity. Increment sensitivity only includes the influence of given sub-domain (increment function of a variable OR its interaction), while the variable sensitivity takes into account all the aspects of a given variable (increment function of the variable AND ALL its interaction effects).

In this program, two sensitivities for an increment function are defined. The first sensitivity is the sensitivity of the mean value, which represents the influence of given increment function on the final expected value. In other words, how selected increment function influences the statistical expected value of the final output. The formula for the mean sensitivity estimation is:

where S_{µk} represents the sensitivity of mean for increment function k, S_{T} stands for the set of all increment functions and µ_{k} represents the k-th increment function. The second sensitivity is the variance sensitivity, which represents the variable’s impact on the final output. Variable’s variance sensitivity is defined as follows:

where S_{ok} represents the sensitivity of variance for increment function k, S_{T} stands for the set of all increment functions and sigma^{2}_{k} represents the variance of k-th increment function. It should be noted that both sensitivities are normalized, thus, their sum equals to 1.

## How to use it: #

Table under *sets *-> *options* refers to variables which should be selected as a first in the visualization process. In order to show the sensitivity of a given variable, one needs to tick the box belonging to the given variable. Also, one can cycle through all variables using the buttons above the plot.

*Fig. 1: Sensitivity analysis – Variables*

### Switches: #

**Select: <variable>:**Choose input variables to be shown in the plot.**Text form:**Sensitivities are shown as a table.

### Buttons: #

**Invert:**Inversion of checked and unchecked tick boxes selected for the visualization.**Select all:**Select all available variables.**Unselect all:**Unselect all available variables.**Apply:**Changes on the graph will take effect after pressing the apply button.

The graph is fully adjustable with *Options*, where:

**Plot title:**Title of the graph**Title font:**Font type and size of the plot title**X Axis Label:**Label of X axis**Y Axis Label:**Label of Y axis**Axis Font:**Font type and size of the plot axis**Legend Font:**Font type and size of the plot title, toggle on/off**Mean sensitivity Color:**Color of mean sensitivity bar, toggle on/off**Variance sensitivity Color:**Color of variance sensitivity bar, toggle on/off

*Fig. 2: Sensitivity analysis – Options*

### Print: #

To store selected results in *File* (upper left corner) select *Save*. It will allow you to browse in folders starting in the project folder. The code automatically selects the format to store visualized results.