How easy is it to do?
This is about balancing simplicity and accuracy of the reduced model. Conservation analysis is an especially important parametric method as it does not incur any error and therefore retains all properties of the original model. It is often therefore recommended as a first step for reduction of a large model. However, finding conserved moieties is not straightforward for larger models and it seldom reduces a model by more than 10-15% [33]. Most methods are automatable, meaning that user input is not required for the algorithm to run. This makes complicated mathematical methods, such as linearisation and lumping of nonlinear models [18], more user friendly and accessible. The simplest method that retains acceptable accuracy is using an Empirical Approximation however it has limitations regarding generality.
Most of the model-order reduction methods require the use of a specialised software, except for the Empirical Approximation method. Many of the algorithms are available as pre-coded toolboxes in MATLAB. For example, the Global Optimisation Toolbox for simulated annealing, Deep Learning Toolbox for ANNs, and the SAFE Toolbox for global sensitivity analysis [53].
In all parametric approaches there is a trade-off between model simplicity (model-order) and its predictive performance. Hasegawa et al. [54] proposed a composite criterion-based approach that couples the two opposing factors together to find the optimal reduced model. For all methods (except conservation analysis) it will be necessary for the user to define the trade-off.