Early Prediction of Creep Failure via Bayesian Inference of Evolving Barriers
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915900704161792 |
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| author | Verano-Espitia, Juan Carlos Mäkinen, Tero Alava, Mikko J. Weiss, Jérôme |
| author_facet | Verano-Espitia, Juan Carlos Mäkinen, Tero Alava, Mikko J. Weiss, Jérôme |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16419 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Early Prediction of Creep Failure via Bayesian Inference of Evolving Barriers Verano-Espitia, Juan Carlos Mäkinen, Tero Alava, Mikko J. Weiss, Jérôme Materials Science 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. |
| title | Early Prediction of Creep Failure via Bayesian Inference of Evolving Barriers |
| topic | Materials Science |
| url | https://arxiv.org/abs/2603.16419 |