  The fact that we can build models from data, and therefore refine our models
with more data from experiments, is usually given for granted in scientific
inquiry. However, how much information can we extract, and how precise can we
expect our learned model to be, if we have only a finite amount of data at our
disposal? Nuclear physics demands an high degree of precision from models that
are inferred from the limited number of nuclei that can be possibly made in the
laboratories.
  In manuscript I will introduce some concepts of computational science, such
as statistical theory of learning and Hamiltonian complexity, and use them to
contextualise the results concerning the amount of data necessary to
extrapolate a mass model to a given precision.
