Shun-Peng Zhu

Machine learning-based probabilistic fatigue assessment of turbine bladed disks under multisource uncertainties

B. Keshtegar, M. Bagheri, C. Luo, 2023, International Journal of Structural Integrity.

Defect driven physics-informed neural network framework for fatigue life prediction of additively manufactured materials

Jin-Chao He, C. Luo, X. Niu, 2023, Philosophical Transactions of the Royal Society A.

Physics-informed machine learning and its structural integrity applications: state of the art

J. Correia, C. Luo, Shun-Peng Zhu, 2023, Philosophical Transactions of the Royal Society A.

Microstructural size effect on the notch fatigue behavior of a Ni-based superalloy using crystal plasticity modelling approach

Jiewei Gao, Jin-Chao He, Shun-Peng Zhu, 2023, International Journal of Plasticity.

Probabilistic notch fatigue assessment under size effect using micromechanics-based critical distance theory

Jin-Chao He, C. Luo, Qingyuan Wang, 2024, International Journal of Fatigue.

Defect sensitivity and fatigue design: Deterministic and probabilistic aspects in AM metallic materials

D. Liao, P. Foti, X. Niu, 2024, Progress in Materials Science.