A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation

DOI10.1016/j.cma.2023.116521MaRDI QIDQ6118588

Alessandro Tognan, Luca Laurenti, Enrico Salvati, Andrea Patanè

Publication date: 21 March 2024

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

zbMATH Keywords

defectsuncertainty quantificationadditive manufacturingfatigue strengthBayesian physics-guided neural networks

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

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