Towards the future of physics- and data-guided AI frameworks in computational mechanics
Article
Article Title | Towards the future of physics- and data-guided AI frameworks in computational mechanics |
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ERA Journal ID | 3656 |
Article Category | Article |
Authors | Bai, Jinshuai, Wang, Yizheng, Jeong, Hyogu, Chu, Shiyuan, Wang, Qingxia, Alzubaidi, Laith, Zhuang, Xiaoying, Rabczuk, Timon, Xie, Yi Min, Feng, Xi-Qiao and Gu, Yuantong |
Journal Title | Acta Mechanica Sinica (Lixue Xuebao) |
Journal Citation | 41 |
Article Number | 225340 |
Number of Pages | 14 |
Year | 2025 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 0459-1879 |
0567-7718 | |
1614-3116 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s10409-025-25340-x |
Web Address (URL) | https://link.springer.com/article/10.1007/s10409-025-25340-x |
Abstract | The integration of physics-based modelling and data-driven artificial intelligence (AI) has emerged as a transformative paradigm in computational mechanics, This perspective reviews the development and current status of AI-empowered frameworks, including data-driven methods, physics-informed neural networks, and neural operators, While these approaches have demonstrated significant promise, challenges remain in terms of robustness, generalisation, and computational efficiency, We delineate four promising research directions: (1) Modular neural architectures inspired by traditional computational mechanics, (2) physics informed neural operators for resolution-invariant operator learning, (3) intelligent frameworks for multiphysics and multiscale biomechanics problems, and (4) structural optimisation strategies based on physics constraints and reinforcement learning, These directions represent a shift toward foundational frameworks that combine the strengths of physics and data, opening new avenues for the modelling, simulation, and optimisation of complex physical systems. |
Keywords | Computational mechanics; Physics-informed neural network; Operator learning; Biomechanics; Topology optimisation |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 401799. Mechanical engineering not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Queensland University of Technology |
Tsinghua University, China | |
School of Business | |
Centre for Applied Climate Sciences | |
Leibniz University Hannover, Germany | |
Bauhaus University Weimar, Germany | |
Hohai University, China |
https://research.usq.edu.au/item/zyx5v/towards-the-future-of-physics-and-data-guided-ai-frameworks-in-computational-mechanics
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