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

TL;DR

This paper introduces Bayesian Hidden Physics Models to discover underlying physics from data and apply it to characterize microstructure and flaws in metallic specimens, demonstrating the potential of data-driven physics discovery.

Contribution

The paper presents a novel application of Bayesian Hidden Physics Models to uncover physics from observational data and transfer that knowledge to characterize microstructure and flaws.

Findings

Learned physics explains backscattering in cracked specimens

Physics discovery enables microstructure characterization

Model accurately predicts acoustic propagation features

Abstract

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.