A neural-network model is developed to reproduce the differences between
experimental nuclear mass-excess values and the theoretical values given by the
Finite Range Droplet Model. The results point to the existence of subtle
regularities of nuclear structure not yet contained in the best
microscopic/phenomenological models of atomic masses. Combining the FRDM and
the neural-network model, we create a hybrid model with improved predictive
performance on nuclear-mass systematics and related quantities.Comment: Proceedings for the 15th Hellenic Symposium on Nuclear Physic