A new composite neural network with spatiotemporal features extraction capability for unsteady flow fields predictions DOI
Cheng Xu, Zhengxian Liu, Xiaojian Li

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(2)

Published: Feb. 1, 2025

Artificial intelligence based on neural network technology has provided innovative methods for predicting unsteady flow fields. However, both purely data-driven and single physics-driven can only perform short-term predictions fields are unable to achieve medium- long-term predictions. A composite CNN-GRU-PINN (CGPINN) is proposed by combining convolutional (CNN), gated recurrent unit (GRU), physics-informed (PINN). CNN GRU used learn the spatial temporal characteristics of flows, respectively. PINN adopted constrain field prediction data according physical laws. The around a circular cylinder employed verify performances CGPINN. test results show that compared PINN, reconstruction accuracy CGPINN improved about 86.10% average, 96.18%. Compared pure approaches, an average 65.71%. Additionally, exhibits better robustness, demonstrating insensitivity variations in sample size noise levels, thereby ensuring stable reliable across diverse conditions. This study more accurate robust method

Language: Английский

A new composite neural network with spatiotemporal features extraction capability for unsteady flow fields predictions DOI
Cheng Xu, Zhengxian Liu, Xiaojian Li

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(2)

Published: Feb. 1, 2025

Artificial intelligence based on neural network technology has provided innovative methods for predicting unsteady flow fields. However, both purely data-driven and single physics-driven can only perform short-term predictions fields are unable to achieve medium- long-term predictions. A composite CNN-GRU-PINN (CGPINN) is proposed by combining convolutional (CNN), gated recurrent unit (GRU), physics-informed (PINN). CNN GRU used learn the spatial temporal characteristics of flows, respectively. PINN adopted constrain field prediction data according physical laws. The around a circular cylinder employed verify performances CGPINN. test results show that compared PINN, reconstruction accuracy CGPINN improved about 86.10% average, 96.18%. Compared pure approaches, an average 65.71%. Additionally, exhibits better robustness, demonstrating insensitivity variations in sample size noise levels, thereby ensuring stable reliable across diverse conditions. This study more accurate robust method

Language: Английский

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