Analysis of data‐driven models for predicting fatigue strength of steel components with uncertainty quantification
Christian Frie,
Anton Kolyshkin,
Chris Eberl
Abstract:Material informatics has emerged as a valuable research field in material science, providing solutions to previously unsolvable problems or accelerating deliverables. Fatigue failure, as a complex and non‐deterministic phenomenon, requires a probabilistic approach to assess the uncertainty of the fatigue strength prediction. This study compares various probabilistic data‐driven models for credible fatigue strength predictions for three distinct steel groups. The analysis considers data and model uncertainty, e… Show more
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