Physics-informed machine learning for loading history dependent fatigue delamination of composite laminates

Yao, Liaojun; Wang, Jiexiong; Chuai, Mingyue; Lomov, Stepan V.; Carvelli, V.

doi:10.1016/j.compositesa.2024.108474

Composites Part A: Applied Science and Manufacturing

2024

DOI: 10.1016/j.compositesa.2024.108474

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Physics-informed machine learning for loading history dependent fatigue delamination of composite laminates

Liaojun Yao,

Jiexiong Wang,

Mingyue Chuai

et al.

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Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review

Khalid,

Yazdani,

Azad

et al. 2024

Mathematics

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Physics-Informed Neural Networks (PINNs) integrate physics principles with machine learning, offering innovative solutions for complex modeling challenges. Laminated composites, characterized by their anisotropic behavior, multi-layered structures, and intricate interlayer interactions, pose significant challenges for traditional computational methods. PINNs address these issues by embedding governing physical laws directly into neural network architectures, enabling efficient and accurate modeling. This review provides a comprehensive overview of PINNs applied to laminated composites, highlighting advanced methodologies such as hybrid PINNs, k-space PINNs, Theory-Constrained PINNs, optimal PINNs, and disjointed PINNs. Key applications, including structural health monitoring (SHM), structural analysis, stress-strain and failure analysis, and multi-scale modeling, are explored to illustrate how PINNs optimize material configurations and enhance structural reliability. Additionally, this review examines the challenges associated with deploying PINNs and identifies future directions to further advance their capabilities. By bridging the gap between classical physics-based models and data-driven techniques, this review advances the understanding of PINN methodologies for laminated composites and underscores their transformative role in addressing modeling complexities and solving real-world problems.

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Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review

Khalid,

Yazdani,

Azad

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

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Physics-informed machine learning for loading history dependent fatigue delamination of composite laminates

Cited by 1 publication

References 47 publications

Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review

Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review

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