74 citations · 83 across the 8 of their papers we have counts for
9 papers
Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
Xintong Zou, Zhijie Li, Yunpeng Wang +2
Turbulence poses challenges for numerical simulation due to its chaotic, multiscale nature and high computational cost. Traditional turbulence modeling often struggles with accurac…
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…
An Implicit Adaptive Fourier Neural Operator for Long-term Predictions of Three-dimensional Turbulence
Yuchi Jiang, Zhijie Li, Yunpeng Wang +2
Long-term prediction of three-dimensional (3D) turbulent flows is one of the most challenging problems for machine learning approaches. Although some existing machine learning appr…
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…
LESnets (Large-Eddy Simulation nets): Physics-informed neural operator for large-eddy simulation of turbulence
Sunan Zhao, Zhijie Li, Boyu Fan +3
Acquisition of large datasets for three-dimensional (3D) partial differential equations (PDE) is usually very expensive. Physics-informed neural operator (PINO) eliminates the high…
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 (…
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…
Long-term predictions of turbulence by implicit U-Net enhanced Fourier neural operator
Zhijie Li, Wenhui Peng, Zelong Yuan +1
Long-term predictions of nonlinear dynamics of three-dimensional (3D) turbulence are very challenging for machine learning approaches. In this paper, we propose an implicit U-Net e…