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Al-Selwi, S.M., Hassan, M.F., Abdulkadir, S.J., Muneer, A., Sumiea, E.H., Alqushaibi, A., et al. (2024) RNN-LSTM: From Applications to Modeling Techniques and Beyond—Systematic Review. Journal of King Saud University—Computer and Information Sciences, 36, Article ID: 102068.
https://doi.org/10.1016/j.jksuci.2024.102068

has been cited by the following article:

• TITLE: Physics-Informed LSTM for Fatigue Life Prediction of Rubber Isolators under Thermo-Mechanical Coupling

  AUTHORS: Shen Liu, Fei Meng

  KEYWORDS: Rubber Isolator, Fatigue Life, PINN, LSTM, Thermo-Mechanical Coupling

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.16 No.4, April 7, 2026

  ABSTRACT: Rubber supports are ubiquitous in modern vibration isolation systems. Their fatigue evolution under coupled thermo-mechanical loading is exceptionally complex. Traditional life prediction methods rely heavily on empirical formulas. These methods often lack accuracy and extrapolation capabilities under varying temperatures. To address this, we propose a novel LSTM-PINN architecture. This framework integrates physical constitutive relations and temperature effects into a neural network. We used transfer learning to extract baseline physical data across wide temperature ranges. Long Short-Term Memory (LSTM) layers capture sequential loading features. We embedded partial differential equations (PDEs) into the loss function. These PDEs are based on strain energy density (SED) and Arrhenius thermodynamics. This approach ensures strict adherence to physical laws. Results demonstrate that LSTM-PINN achieves high precision even with small datasets. It also exhibits superior out-of-distribution (OOD) generalization. This framework provides a new paradigm for evaluating the reliability of rubber components.