Uncertainty Quantification and Bayesian Active Learning for Rupture Life Prediction in Ferritic-Martensitic Steels

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 for the holdout test set) in addition to meaningful uncertainty estimate (i.e., coverage ranges from 94 – 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.