Modelling of parametrized processes via regression in the model space of neural networks

We 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.