  We present the concepts of physics-based learning models (PBLM) and their
relevance and application to the field of ship hydrodynamics. The utility of
physics-based learning is motivated by contrasting generic learning models for
regression predictions, which do not presume any knowledge of the system other
than the training data provided with methods such as semi-empirical models,
which incorporate physical insights along with data-fitting. PBLM provides a
framework wherein intermediate models, which capture (some) physical aspects of
the problem, are incorporated into modern generic learning tools to
substantially improve the predictions of the latter, minimizing the reliance on
costly experimental measurements or high-resolution high-fidelity numerical
solutions. To illustrate the versatility and efficacy of PBLM, we present three
wave-ship interaction problems: 1) at speed waterline profiles; 2) ship motions
in head seas; and 3) three-dimensional breaking bow waves. PBLM is shown to be
robust and produce error rates at or below the uncertainty in the generated
data at a small fraction of the expense of high-resolution numerical
predictions.
