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Strategies and applications for predicting flow using neural networks: a review

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Abstract

This paper introduces the current state of neural network technology for predicting fluid flow. In particular, we introduce (i) neural networks for spatiotemporal flow field predictions, (ii) neural networks that can learn from a small number of fluid data points, and (iii) aero- and hydrodynamic applications of artificial neural networks. The first topic discusses research on predicting unsteady flow fields using convolutional neural networks and generative adversarial networks. The second topic covers methods to increase the learning performance of neural networks when only a limited amount of data is available due to the high cost of fluid simulations and experiments. In the third topic, examples of applying neural networks in the fields of wind power and meteorology are introduced. This article will present the challenges faced in fluid flow prediction based on neural networks, as well as expectations for the positive changes that future technological advances will bring.

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The authors declare that the data supporting the findings of this study are available within the paper.

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Acknowledgements

This study was conducted with the support of Inha University Research Grant. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1F1A1066547). This research was supported by Korea Institute for Technology Evaluation and Planning (KETEP) grant funded by the Korea Government (MOTIE) (RS-2023-00243974, Program of Energy manpower fostering business).

Funding

This research was supported by Inha University, the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1F1A1066547), and the Korea Institute for Technology Evaluation and Planning (KETEP) grant funded by the Korea Government (MOTIE) (RS-2023-00243974, Program of Energy manpower fostering business).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Inha University, Incheon, 22212, Republic of Korea

    Jiwon Kang, Heesoo Shin & Sangseung Lee

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  1. Jiwon Kang
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  2. Heesoo Shin
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Correspondence to Sangseung Lee.

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Kang, J., Shin, H. & Lee, S. Strategies and applications for predicting flow using neural networks: a review. JMST Adv. 6, 55–60 (2024). https://doi.org/10.1007/s42791-024-00066-0

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