For the original loss function, the graphs in figure show that the most balanced inter-domain weighting factor distribution is reached when most of the weight (∼ 95 %) is assigned to the density. However, for this setup, the errors for the density reproduction are large with the best values lying between 2.80 % to 5.90 % (see tables 2, 3, 4 and 5 in section 5). Further analyses of the loss function suggest that applying a non-linear projection (NLP) to the individual parts of the loss function is beneficial, increasing the comparability of the particular optimization objectives. The NLP ensures that the loss function values for the particular objectives have the same order of magnitude, once the sensitivity hyperparameter c is configured appropriately.