PSI - Issue 79

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

305

Zhou, Shuwei, Henrich, Manuel, Wei, Zhichao, Feng, Feng, Yang, Bing, Mu¨nstermann, Sebastian, 2025. A general physics-informed neural net work framework for fatigue life prediction of metallic materials. Engineering Fracture Mechanics, 322, 111136. He, GaoYuan, Zhao, YongXiang, Yan, ChuLiang, 2023. MFLP. European Journal of Mechanics - A / Solids, 98, 104889. Chen, Dong, Li, Yazhi, Liu, Ke, Li, Yi, 2023. A physics-informed neural network approach to fatigue life prediction using small quantity of samples. International Journal of Fatigue, 166, 107270. Bartosˇa´k, Michal, Halamka, Jiˇr´ı, Bera´nek, Libor, Koukol´ıkova´, Martina, Slany´, Michal, Paga´cˇ, Marek, Dzˇugan, Jan, 2025. Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L. International Journal of Fatigue, 190, 108608. Halamka, Jiˇr´ı, Bartosˇa´k, Michal, Sˇ paniel, Miroslav, 2023. Using hybrid physics-informed neural networks to predict lifetime under multiaxial fatigue loading. Engineering Fracture Mechanics, 289, 109351. Kingma, Diederik P. and Ba, Jimmy, 2017. Adam: A. arXiv. Glorot, Xavier and Bengio, Yoshua, 2010. Understanding the di ffi culty of training deep feedforward neural networks. JMLR Workshop and Con ference Proceedings, 2010. p. 249-256. Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Ko¨pf, Andreas and Yang, Edward and DeVito, Zach and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith, 2019. PyTorch. arXiv.

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