Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models
This work addresses the challenge of automated scientific discovery and material characterization for researchers in physics and materials science, representing a novel application rather than an incremental improvement.
The paper tackles the problem of discovering physical laws from data and using them to characterize material microstructure, demonstrating that physics learned from a pristine metallic specimen can explain backscattering in a cracked sample, a qualitative feature absent in the original data.
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.