Early Prediction of Creep Failure via Bayesian Inference of Evolving Barriers

Abstract

Creep under a sustained load can persist for long times yet culminate in abrupt yielding or rupture, implying a finite lifetime even when the material appears solid. Here, we formulate lifetime prediction as Bayesian inference over an evolving activation-energy landscape. A time-dependent distribution of activation barriers controls deformation: stress lowers barriers, while irreversible rearrangements deplete the weakest sites and reshape the low-barrier tail. Using early-time acoustic emission data, Bayesian inference estimates the evolving barrier statistics in each sample and yields posterior predictive distributions for the time-to-failure. This approach provides online uncertainty-aware lifetime forecasts -- already at around 10~\% of the sample lifetime -- that link microscopic barrier evolution to macroscopic creep dynamics.