The loss-curve (MSE) for network training with both sampling strategies is depicted in Figure From Figure , it is apparent that the performance of the presented network on the small data sets (SU and SL) does not significantly depend on the chosen sampling strategy. The network trained on SL data set performs marginally better on both the SU and SL test data set when measured by MRE (compare columns MRE for SL and SU data) but the effect is near negligible. This is, however, not the case for networks trained on the large data sets (LU and LL) where the sampling strategy has a big impact on network performance (compare SI). Under these circumstances the network trained on the LL dataset outperforms the LU-trained one under all conditions and metrics. For the larger dataset this promises better generalizability for the network trained on log-uniformly sampled data. This behavior may be explained by the pressure range considered in the small or large data sets: While uniformly sampling in the range of 1 -1000 bar severely underrepresents low pressures, logarithmically uniform sampling in the range of 1 -100 bar is reasonably close toand achievable byuniform sampling as well and thus provides no significant benefit.