Augmenting fatigue-life ML models with Basquin-model features and boundary losses is claimed to improve prediction and uncertainty quantification, but key equations and one results table contain errors.
Evolution of artificial intelligence for application in contemporary materials science
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning
Augmenting fatigue-life ML models with Basquin-model features and boundary losses is claimed to improve prediction and uncertainty quantification, but key equations and one results table contain errors.