Statistical modeling of data sets by neural-network techniques is offered as an alternative to traditional semiempirical approaches to global modeling of nuclear properties. New results are presented to support the position that such novel techniques can rival conventional theory in predictive power, if not in economy of description. Examples include the statistical inference of atomic masses a...

Nuclear Astrophysics requires the knowledge of reaction rates over a wide range of nuclei and temperatures. In recent calculations the nuclear level density – as an important ingredient to the statistical model (Hauser-Feshbach) – has shown the highest uncertainties. In a back-shifted fermi-Gas formalism utilizing an energy-dependent level density parameter and employing microscopic corrections...