Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials of the aerospace industry

MLA

Ismael Ben-Yelun, et al. “Self-Learning Locally-Optimal Hypertuning Using Maximum Entropy, and Comparison of Machine Learning Approaches for Estimating Fatigue Life in Composite Materials of the Aerospace Industry.” Engineering Structures, vol. 283, May 2023, p. 115829. EBSCOhost, https://doi.org/10.1016/j.engstruct.2023.115829.

APA

Ismael Ben-Yelun, Miguel Díaz-Lago, Luis Saucedo-Mora, Miguel Ángel Sanz, Ricardo Callado, & Francisco Javier Montáns. (2023). Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials of the aerospace industry. Engineering Structures, 283, 115829. https://doi.org/10.1016/j.engstruct.2023.115829

Chicago

Ismael Ben-Yelun, Miguel Díaz-Lago, Luis Saucedo-Mora, Miguel Ángel Sanz, Ricardo Callado, and Francisco Javier Montáns. 2023. “Self-Learning Locally-Optimal Hypertuning Using Maximum Entropy, and Comparison of Machine Learning Approaches for Estimating Fatigue Life in Composite Materials of the Aerospace Industry.” Engineering Structures 283 (May): 115829. doi:10.1016/j.engstruct.2023.115829.