But we are also studying the possibility to use Surrogate Models (SuM). SuMs are easy-to-compute compact models that mimic the complex behaviour of the models in special ranges used in a simulation. Surrogate models are constructed using data-driven approaches as we know from machine learning in our mlf . Simplified: run the simulation with the "original" models, trying to cover the whole working-space, sample the results and extract the SUMs from them.
The finding process is time consuming, because you need to do massive model and cross-model testing.
In Mathematica's declarative environment it is easy to define higher level tasks, as in mlf, where one can define such machine learning tasks, which then run automatically.