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There has been a surge in the interest of using machine learning techniquesto assist in the scientific process of formulating knowledge to explainobservational data. We demonstrate the use of Bayesian Hidden Physics Models tofirst uncover the physics governing the propagation of acoustic impulses inmetallic specimens using data obtained from a pristine sample. We then use thelearned physics to characterize the microstructure of a separate specimen witha surface-breaking crack flaw. Remarkably, we find that the physics learnedfrom the first specimen allows us to understand the backscattering observed inthe latter sample, a qualitative feature that is wholly absent from thespecimen from which the physics were inferred. The backscattering is explainedthrough inhomogeneities of a latent spatial field that can be recognized as thespeed of sound in the media.

Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models