Neural networks are limited in situations that differ from the learned conditions. We propose a 3-step solution to this out of distribution problem: (1) Derive the anomalies of a trained neural networks' internal parameters by retraining on subsets of its training data. (2) Relate the ensuing network-parameter anomalies to differences in the training data subsets that caused them. (3) Extrapolate the found relations to generate a network that performs better outside the org. training distribution.