Aswolinskiy, W.W.AswolinskiyReinhart, R.F.R.F.ReinhartSteil, J.J.J.J.Steil2022-03-052022-03-052017https://publica.fraunhofer.de/handle/publica/25204810.1016/j.neucom.2016.12.086We consider the modelling of parametrized processes, where the goal is to model the process for new parameter value combinations. We compare the classical regression approach to a modular approach based on regression in the model space: First, for each process parametrization a model is learned. Second, a mapping from process parameters to model parameters is learned. We evaluate both approaches on two synthetic and two real-world data sets and show the advantages of the regression in the model space.en006journal article