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Neural Computing and Applications

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07-03-2022 | Original Article

Navier–stokes Generative Adversarial Network: a physics-informed deep learning model for fluid flow generation

Authors: Pin Wu, Kaikai Pan, Lulu Ji, Siquan Gong, Weibing Feng, Wenyan Yuan, Christopher Pain

Published in: Neural Computing and Applications | Issue 14/2022

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Abstract

Numerical simulation in Computational Fluid Dynamics mainly relies on discretizing the governing equations in time or space to obtain numerical solutions, which is expensive and time-consuming. Deep learning significantly reduces the computational cost due to its great nonlinear curve fitting capability, however, the data-driven models is agnostic to latent relationships between input and output. In this paper, a novel deep learning named Navier–Stokes Generative Adversarial Network integrated with physical information is proposed. The Navier–Stokes Equation is added to the loss function of the generator in the form of residuals, which means physics loss in this paper. Then, the proposed model is trained in the framework of generative adversarial network. Experimental results show that proposed model significantly outperform similar models, mean absolute error are decreased by 62.29, 78.42 and 78.61% on pressure and velocity components. What’s more, effectiveness of introducing physics loss is also verified.

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Title: Navier–stokes Generative Adversarial Network: a physics-informed deep learning model for fluid flow generation
Authors: Pin Wu
Kaikai Pan
Lulu Ji
Siquan Gong
Weibing Feng
Wenyan Yuan
Christopher Pain
Publication date: 07-03-2022
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 14/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-07042-6

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