Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels

Journal Article · 07 February 2022 · Scientific Reports

DOI: https://doi.org/10.1038/s41598-022-06051-8 · OSTI ID:1844035

Mamun, Osman; Taufique, M. F. N.; Wenzlick, Madison; Hawk, Jeffrey; Devanathan, Ram

Abstract Three probabilistic methodologies are developed for predicting the long-term creep rupture life of 9–12 wt%Cr ferritic-martensitic steels using their chemical and processing parameters. The framework developed in this research strives to simultaneously make efficient inference along with associated risk, i.e., the uncertainty of estimation. The study highlights the limitations of applying probabilistic machine learning to model creep life and provides suggestions as to how this might be alleviated to make an efficient and accurate model with the evaluation of epistemic uncertainty of each prediction. Based on extensive experimentation, Gaussian Process Regression yielded more accurate inference ( $$$$Pearson\;correlation\;coefficent> 0.95$$$$ $Pearsoncorrelationcoefficent>0.95$ for the holdout test set) in addition to meaningful uncertainty estimate (i.e., coverage ranges from 94 to 98% for the test set) as compared to quantile regression and natural gradient boosting algorithm. Furthermore, the possibility of an active learning framework to iteratively explore the material space intelligently was demonstrated by simulating the experimental data collection process. This framework can be subsequently deployed to improve model performance or to explore new alloy domains with minimal experimental effort.

• Sponsoring Organization: USDOE
• OSTI ID: 1844035
• Journal Information: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 12; ISSN 2045-2322
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English