Study the sensitivity of a model:
Get the value of the parameter:
Find the peak deviation when varying the parameter:
Show a 5% sensitivity bound and the peak deviation time:
Find out which variable is most sensitive to a parameter:
Simulate with sensitivities to a frequency parameter:
A 10% sensitivity bound shows that

is most sensitive to the parameter:
Calibrate parameters in a model by comparing to measurement data:
Set up caching for simulation:
Use

to get gradients:
Fit parameters to the measurement data:
Not using gradients takes longer:
Simulate with the fitted parameters:
Show the test data and the calibrated model together:
Plot a solution with its sensitivity bounds:
Get the nominal value of the parameter:
Show a 5% sensitivity bound:
Simulate with a maximal variation of 5%:
Get the trajectories:
Show that the trajectories are mostly contained in the approximated sensitivity bounds: