PSI - Issue 79

Henrik Petersson et al. / Procedia Structural Integrity 79 (2026) 298–305

304

Fig. 3. Experimental data and prediction of fatigue life, with the 95% probability interval for the PINN prediction when data is removed.

Acknowledgements

This research was carried out with the support of Sweden’s Innovation Agency (Vinnova) within the Advanced Digitalisation program, project number 2024-00221.

References

Raissi, M., Perdikaris, P., Karniadakis, G.E., 2019. Physics-informed neural networks: A. Journal of Computational Physics, 378, 686–707. Faroughi, Salah A., Pawar, Nikhil M., Fernandes, Ce´lio, Raissi, Maziar, Das, Subasish, Kalantari, Nima K., Kourosh Mahjour, Seyed, 2024. Physics-Guided. Journal of Computing and Information Science in Engineering, 24, 040802. Glorot, Xavier, Bengio, Yoshua, n.d.. Understanding the di ffi culty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:249-256, 2010. Dantas, Pierre Vilar, Sabino Da Silva, Waldir, Cordeiro, Lucas Carvalho, Carvalho, Celso Barbosa, 2024. A comprehensive review of model compression techniques in machine learning. Appl Intell, 54, 11804–11844. Gbagba, Sadiq, Maccioni, Lorenzo, Concli, Franco, 2023. Advances in Machine. Applied Sciences, 14, 398. Quraishy, Mohammed Shahbaz, Kundu, Tarun Kumar, 2025. A Comprehensive. Journal of Materials Engineering and Performance, , . Zhang, Xiao-Cheng, Gong, Jian-Guo, Xuan, Fu-Zhen, 2021. A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions. International Journal of Fatigue, 148, 106236. Singh, Vishal, Harursampath, Dineshkumar, Dhawan, Sharanjeet, Sahni, Manoj, Saxena, Sahaj, Mallick, Rajnish, 2024. Physics-Informed. Mod elling, 5, 1532–1549. Haghighat, Ehsan, Raissi, Maziar, Moure, Adrian, Gomez, Hector, Juanes, Ruben, 2021. A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics. Computer Methods in Applied Mechanics and Engineering, 379, 113741. Yang, Dexin, Jin, Afang, Li, Yun, 2024. A Novel. Applied Sciences, 14, 2502. Wang, Haijie, Li, Bo, Gong, Jianguo, Xuan, Fu-Zhen, 2023. Machine learning-based fatigue life prediction of metal materials: Perspectives. Engineering Fracture Mechanics, 284, 109242.

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