Abstract
In order to offer guidelines for physics-informed neural network (PINN) implementation, this study presents a comprehensive review of PINN, an emerging field at the intersection of deep learning and computational physics. PINN offers a novel approach to solve physics problems by leveraging the flexibility and scalability of neural networks, even with small or no data. First, a general description of different physics problem types and target tasks addressable with PINN was provided. A generic PINN architecture was described in detail using a component-wise approach, with components ranging from collocation points to optimization methods. Then, we surveyed studies that sought to improve upon each of these components. To offer practical insights, we highlighted studies that focused on key issues of PINN implementation and showcased three practical applications. Lastly, a summary and potential research directions were provided to offer guidelines for reliable and customized PINN implementations.
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This research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries (20220210).
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Ikhyun Ryu received a B.S. degree in Mechanical Engineering from Karlsruhe Institute of Technology, Karlsruhe, Germany, in 2019. He then received a M.S. degree in Mechanical Engineering from Hanyang University, Seoul, South Korea, in 2022. He is now a Senior Research Engineer in PIDOTECH Inc. since 2022. His research interests include heat transfer, multidisciplinary design optimization, and Physics-Informed Neural Networks.
Gyu-Byung Park received a B.S. degree in Mechanical Engineering from Hanyang University, South Korea, in 2006. He then received a Ph.D. degree in Mechanical Engineering from Hanyang University, South Korea, in 2016. He is now a Senior Research Engineer in PIDOTECH Inc. since 2016. His research interests include Artificial Intelligence, Deep Learning, Design Optimization and Physics-Informed Neural Networks.
Yongbin Lee received a B.S. degree in Mechanical Engineering from Hanyang University, Seoul, South Korea, in 2002. He then received a M.S. degree in Mechanical Engineering from Hanyang University, Seoul, South Korea, in 2004. He then received a Ph.D. degree in Mechanical Engineering from Hanyang University, Seoul, South Korea, in 2009. He is now a Senior Research Engineer in PIDOTECH since 2012. His research interests include Machine Learning, Metamodeling, Design of Experiments, and Design Optimization.
Dong-Hoon Choi graduated from Seoul National University with a B.S. in mechanical engineering in 1975. He then graduated from KAIST in 1977 with an M.S. in Mechanical Engineering. He earned his Ph.D. in mechanical engineering from the University of Wisconsin-Madison. From 1986 to 2018, he served as a Professor of Mechanical Engineering at Hanyang University. He has been the CEO of PIDOTECH Inc. since 2003. His research interest includes AI-aided design optimization, multidisciplinary design optimization, surrogate-based design optimization, and AI applications for simulation and engineering design.
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Ryu, I., Park, GB., Lee, Y. et al. Physics-informed neural network for engineers: a review from an implementation aspect. J Mech Sci Technol 38, 3499–3519 (2024). https://doi.org/10.1007/s12206-024-0624-9
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DOI: https://doi.org/10.1007/s12206-024-0624-9