  There has been a surge in the interest of using machine learning techniques
to assist in the scientific process of formulating knowledge to explain
observational data. We demonstrate the use of Bayesian Hidden Physics Models to
first uncover the physics governing the propagation of acoustic impulses in
metallic specimens using data obtained from a pristine sample. We then use the
learned physics to characterize the microstructure of a separate specimen with
a surface-breaking crack flaw. Remarkably, we find that the physics learned
from the first specimen allows us to understand the backscattering observed in
the latter sample, a qualitative feature that is wholly absent from the
specimen from which the physics were inferred. The backscattering is explained
through inhomogeneities of a latent spatial field that can be recognized as the
speed of sound in the media.
