APA (7th ed.) Citation

Li, H., Sun, G., Tian, Z., Huang, K., & Zhao, Z. (2024). A physics‐informed neural network framework based on fatigue indicator parameters for very high cycle fatigue life prediction of an additively manufactured titanium alloy. Fatigue & Fracture of Engineering Materials & Structures, 47(9), 3171. https://doi.org/10.1111/ffe.14363

Chicago Style (17th ed.) Citation

Li, Hang, Guanze Sun, Zhao Tian, Kezhi Huang, and Zihua Zhao. "A Physics‐informed Neural Network Framework Based on Fatigue Indicator Parameters for Very High Cycle Fatigue Life Prediction of an Additively Manufactured Titanium Alloy." Fatigue & Fracture of Engineering Materials & Structures 47, no. 9 (2024): 3171. https://doi.org/10.1111/ffe.14363.

MLA (9th ed.) Citation

Li, Hang, et al. "A Physics‐informed Neural Network Framework Based on Fatigue Indicator Parameters for Very High Cycle Fatigue Life Prediction of an Additively Manufactured Titanium Alloy." Fatigue & Fracture of Engineering Materials & Structures, vol. 47, no. 9, 2024, p. 3171, https://doi.org/10.1111/ffe.14363.