3 citations · 3 across the 1 of their papers we have counts for
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Machine-learning-based simulation of turbulent flows over periodic hills using a hybrid U-Net and Fourier neural operator framework
Yunpeng Wang, Huiyu Yang, Zelong Yuan +3
Simulating massively separated turbulent flows over bodies is one of the major applications for large-eddy simulation (LES). In the current work, we propose a machine-learning-base…
Residual U-Net for accurate and efficient prediction of hemodynamics in two-dimensional asymmetric stenosis
Xintong Zou, Suiyang Tong, Wenhui Peng +2
This study presents residual U-Net (U-ResNet), a deep learning surrogate model for predicting steady hemodynamic fields in two-dimensional asymmetric stenotic channels at Reynolds…
Prediction of three-dimensional chemically reacting compressible turbulence based on implicit U-Net enhanced Fourier neural operator
Zhiyao Zhang, Zhijie Li, Yunpeng Wang +4
The accurate and fast prediction of long-term dynamics of turbulence presents a significant challenge for both traditional numerical simulations and machine learning methods. In re…
Prediction of turbulent channel flow using Fourier neural operator-based machine-learning strategy
Yunpeng Wang, Zhijie Li, Zelong Yuan +3
Fast and accurate predictions of turbulent flows are of great importance in the science and engineering field. In this paper, we investigate the implicit U-Net enhanced Fourier neu…
Fourier neural operator for large eddy simulation of compressible Rayleigh-Taylor turbulence
Tengfei Luo, Zhijie Li, Zelong Yuan +5
The Fourier neural operator (FNO) framework is applied to the large eddy simulation (LES) of three-dimensional compressible Rayleigh-Taylor (RT) turbulence with miscible fluids at…
A transformer-based neural operator for large-eddy simulation of turbulence
Zhijie Li, Tianyuan Liu, Wenhui Peng +2
Predicting the large-scale dynamics of three-dimensional (3D) turbulence is challenging for machine learning approaches. This paper introduces a transformer-based neural operator (…