A novel spatial–temporal prediction method for the effects of fish movement on flow field based on hybrid deep neural network-Reference-Cited by-同舟云学术

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A novel spatial–temporal prediction method for the effects of fish movement on flow field based on hybrid deep neural network

Published:2024-07-04 Issue:1 Volume:18 Page:
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

Wang Jingyang¹,Baiyinbaoligao ¹,Mu Xiangpeng¹,Qie Zhihong²,Xu Fengran¹,Li Xiaochen¹

Affiliation:

1. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, People’s Republic of China

2. College of Urban and Rural Construction, Hebei Agricultural University, Baoding, People’s Republic of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2024.2370927

Reference36 articles.

1. Fast vacuum plume prediction using a convolutional neural networks-based direct simulation Monte Carlo method

2. The swimming patterns and energy-saving mechanism revealed from three fish in a school

3. Curatolo, M., & Teresi, L. (2015). The virtual aquarium: Simulations of fish swimming. The Proceedings of the 2015 COMSOL Conference.

4. Collective behaviour of fish in the presence and absence of flow

5. Deep neural networks for nonlinear model order reduction of unsteady flows

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