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Uncertainty in Creep Life Prediction by aaron is a document available to read on EtoBox.

This document presents three probabilistic methodologies for predicting the long-term creep rupture life of 9–12 wt%Cr ferritic-martensitic steels, focusing on uncertainty quantification in Bayesian active learning. The study demonstrates that Gaussian Process Regression outperforms other methods in accuracy and uncertainty estimation, while also showcasing a batch-mode active learning framework to optimize data collection for model improvement. The research aims to enhance the reliability of predictive mod

Author

aaron

Language

EN