Error minimization is conducted using the well known backpropagation algorithm coupled with the ADAMoptimizer for network parameter optimization . The initial learning rate was chosen to be lr = 4×10 -3 with a custom decay schedule reducing it by a factor of λ = 0.72 whenever no significant training success is achieved over 80 epochs. Identification of suitable network and training hyperparameters was conducted preliminary on test problems using Bayesian optimization to minimize the loss on the holdout data set. Training proceeds for 8000 epochs after which no significant reduction of loss was observed for most experiments conducted. After the training epochs, one test step is performed on the holdout data set. During this step, L 1 -loss is calculated as well. However, since interpreting L 1 -loss in terms of prediction accuracy is difficult, another measure on the network parameters is proposed.