Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review
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 signifi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/1/17 |
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| author | Salman Khalid Muhammad Haris Yazdani Muhammad Muzammil Azad Muhammad Umar Elahi Izaz Raouf Heung Soo Kim |
| author_facet | Salman Khalid Muhammad Haris Yazdani Muhammad Muzammil Azad Muhammad Umar Elahi Izaz Raouf Heung Soo Kim |
| author_sort | Salman Khalid |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-673c804dedab429bba73798b4b0296ad |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-673c804dedab429bba73798b4b0296ad2025-01-10T13:17:58ZengMDPI AGMathematics2227-73902024-12-011311710.3390/math13010017Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive ReviewSalman Khalid0Muhammad Haris Yazdani1Muhammad Muzammil Azad2Muhammad Umar Elahi3Izaz Raouf4Heung Soo Kim5Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaPhysics-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.https://www.mdpi.com/2227-7390/13/1/17physics-informed neural networkslaminated compositesstructural health monitoringmulti-scale modelingstructural analysiscomposite material optimization |
| spellingShingle | Salman Khalid Muhammad Haris Yazdani Muhammad Muzammil Azad Muhammad Umar Elahi Izaz Raouf Heung Soo Kim Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review Mathematics physics-informed neural networks laminated composites structural health monitoring multi-scale modeling structural analysis composite material optimization |
| title | Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review |
| title_full | Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review |
| title_fullStr | Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review |
| title_full_unstemmed | Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review |
| title_short | Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review |
| title_sort | advancements in physics informed neural networks for laminated composites a comprehensive review |
| topic | physics-informed neural networks laminated composites structural health monitoring multi-scale modeling structural analysis composite material optimization |
| url | https://www.mdpi.com/2227-7390/13/1/17 |
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