Calibrate
Calibrating models to data, or finding parameters which make a model a sufficiently close fit to data, is part of the core functionality of Pumas-QSP. The calibrate
function is designed as an automated mechanism for performing such model calibration in an accurate and efficient manner. It uses alternative cost functions such as multiple shooting and collocations to improve the stability of the training process, along with mixing in techniques for sampling initial conditions, to ensure that an accurate fit can be generate with little to no effort.
Note:
calibrate
is designed for finding a single best fitting parameter set for a model. For a set of potential fits to characterize fitting uncertainty, see the documentation on thevpop
virtual population functionality
The calibrate
Function
JuliaSimModelOptimizer.calibrate
— Functioncalibrate(prob, alg; adtype = Optimization.AutoForwardDiff())
Find the best parameters to solve the inverse problem prob
using the calibration algorithm given by alg
.
Arguments
prob
: theInverseProblem
to solvealg
: the calibration algorithm used for building the loss function
Keyword Arguments
adtype
: Automatic differentiation choice, see the
Optimization.jl docs for details. Defaults to AutoForwardDiff()
.
Utility functions
JuliaSimModelOptimizer.get_model
— Functionfunction get_model(res::CalibrateResult)
This function retrieves the model associated with a CalibrateResult
.