Developing physics-informed neural networks for virtual sensing in beams with moving loads DOI Creative Commons
Anmar I. F. Al-Adly, Prakash Kripakaran

Engineering Structures, Journal Year: 2025, Volume and Issue: 338, P. 120535 - 120535

Published: May 27, 2025

Language: Английский

Physics-Informed Neural Network (PINN) Evolution and Beyond: A Systematic Literature Review and Bibliometric Analysis DOI Creative Commons
Zaharaddeen Karami Lawal, Hayati Yassin,

Daphne Teck Ching Lai

et al.

Big Data and Cognitive Computing, Journal Year: 2022, Volume and Issue: 6(4), P. 140 - 140

Published: Nov. 21, 2022

This research aims to study and assess state-of-the-art physics-informed neural networks (PINNs) from different researchers’ perspectives. The PRISMA framework was used for a systematic literature review, 120 articles the computational sciences engineering domain were specifically classified through well-defined keyword search in Scopus Web of Science databases. Through bibliometric analyses, we have identified journal sources with most publications, authors high citations, countries many publications on PINNs. Some newly improved techniques developed enhance PINN performance reduce training costs slowness, among other limitations, been highlighted. Different approaches introduced overcome limitations In this categorized proposed methods into Extended PINNs, Hybrid Minimized Loss techniques. Various potential future directions are outlined based solutions.

Language: Английский

Citations

76
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics DOI
Salah A. Faroughi, Nikhil M. Pawar, Célio Fernandes

et al.

Journal of Computing and Information Science in Engineering, Journal Year: 2024, Volume and Issue: 24(4)

Published: Jan. 8, 2024

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep push scientific forward a range of disciplines, such as fluid mechanics, solid materials science, etc. The incorporation neural networks is particularly crucial this hybridization process. Due their intrinsic architecture, conventional cannot be successfully trained scoped when data are sparse, which the case many engineering domains. Nonetheless, provide foundation respect physics-driven or knowledge-based constraints during training. Generally speaking, there three distinct network frameworks enforce underlying physics: (i) physics-guided (PgNNs), (ii) physics-informed (PiNNs), (iii) physics-encoded (PeNNs). These methods advantages for accelerating numerical modeling complex multiscale multiphysics phenomena. In addition, recent developments operators (NOs) add another dimension these new simulation paradigms, especially real-time prediction systems required. All models also come with own unique drawbacks limitations that call further fundamental research. This study aims present review four (i.e., PgNNs, PiNNs, PeNNs, NOs) used state-of-the-art architectures applications reviewed, discussed, future research opportunities presented terms improving algorithms, considering causalities, expanding applications, coupling solvers.

Language: Английский

Citations

49
AT-PINN-HC: A refined time-sequential method incorporating hard-constraint strategies for predicting structural behavior under dynamic loads DOI
Zhaolin Chen, S.K. Lai, Zhicheng Yang

et al.

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 436, P. 117691 - 117691

Published: Jan. 10, 2025

Language: Английский

Citations

4
Developing a digital twin for dam safety management DOI

S.W. Sun H. Ding,

Pan Jia-zheng, Yanli Wang

et al.

Computers and Geotechnics, Journal Year: 2025, Volume and Issue: 180, P. 107120 - 107120

Published: Jan. 31, 2025

Language: Английский

Citations

2
AT-PINN: Advanced time-marching physics-informed neural network for structural vibration analysis DOI
Zhaolin Chen, S.K. Lai, Zhichun Yang

et al.

Thin-Walled Structures, Journal Year: 2023, Volume and Issue: 196, P. 111423 - 111423

Published: Dec. 2, 2023

Language: Английский

Citations

24
A practical PINN framework for multi-scale problems with multi-magnitude loss terms DOI
Yong Wang, Yanzhong Yao, Jiawei Guo

et al.

Journal of Computational Physics, Journal Year: 2024, Volume and Issue: 510, P. 113112 - 113112

Published: May 17, 2024

Language: Английский

Citations

17
Physics-informed identification of PDEs with LASSO regression, examples of groundwater-related equations DOI
Yang Zhan, Zhilin Guo, Bicheng Yan

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 638, P. 131504 - 131504

Published: June 15, 2024

Language: Английский

Citations

8
Solving fluid flow in discontinuous heterogeneous porous media and multi-layer strata with interpretable physics-encoded finite element network DOI Creative Commons
Xi Wang, Wei Wu,

Hehua Zhu

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

Citations

7
Solving groundwater flow equation using physics-informed neural networks DOI
Salvatore Cuomo, Mariapia De Rosa, Fabio Giampaolo

et al.

Computers & Mathematics with Applications, Journal Year: 2023, Volume and Issue: 145, P. 106 - 123

Published: June 24, 2023

Language: Английский

Citations

16
A mixed pressure-velocity formulation to model flow in heterogeneous porous media with physics-informed neural networks DOI
François Lehmann, Marwan Fahs, Ali Alhubail

et al.

Advances in Water Resources, Journal Year: 2023, Volume and Issue: 181, P. 104564 - 104564

Published: Oct. 23, 2023

Language: Английский

Citations

16