88 citations · 274 across the 16 of their papers we have counts for
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Physics informed wavelet Fourier representation for multiscale fluid dynamics
Chao Wang, Shilong Li, Yunpeng Wang +4
Multiscale fluid flows often contain localized flow structures, such as viscous shock layers, wet-dry fronts, steady viscous wakes, decaying vortical structures, and vortex-sheddin…
Self-scaling tensor basis neural network for Reynolds stress modeling of wall-bounded turbulence
Zelong Yuan, Yuzhu Pearl Li
Recent advances in data-driven turbulence modeling have established tensor basis neural networks (TBNN) as a physically grounded framework for Reynolds-stress closure in Reynolds-a…
Physics-informed Fourier Basis Neural Network for Fluid Mechanics
Chao Wang, Shilong Li, Zelong Yuan +1
Solving partial differential equations (PDEs) is an important yet challenging task in fluid mechanics. In this study, we embed an improved Fourier series into neural networks and p…
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…
Physics-informed Kolmogorov-Arnold Network with Chebyshev Polynomials for Fluid Mechanics
Chunyu Guo, Lucheng Sun, Shilong Li +2
Solving partial differential equations (PDEs) is essential in scientific forecasting and fluid dynamics. Traditional approaches often incur expensive computational costs and trade-…
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…
The discrete direct deconvolution model in the large eddy simulation of turbulence
Ning Chang, Zelong Yuan, Yunpeng Wang +1
The discrete direct deconvolution model (D3M) is developed for the large-eddy simulation (LES) of turbulence. The D3M is a discrete approximation of previous direct deconvolution m…
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…
Ensemble data assimilation-based mixed subgrid-scale model for large-eddy simulations
Yunpeng Wang, Zelong Yuan, Jianchun Wang
An ensemble Kalman filter (EnKF)-based mixed model (EnKF-MM) is proposed for the subgrid-scale (SGS) closure in the large-eddy simulation (LES) of turbulence. The model coefficient…
Adjoint-based variational optimal mixed models for large-eddy simulation of turbulence
Zelong Yuan, Yunpeng Wang, Xiaoning Wang +1
An adjoint-based variational optimal mixed model (VOMM) is proposed for subgrid-scale (SGS) closure in large-eddy simulation (LES) of turbulence. The stabilized adjoint LES equatio…
Linear attention coupled Fourier neural operator for simulation of three-dimensional turbulence
Wenhui Peng, Zelong Yuan, Zhijie Li +1
Modeling three-dimensional (3D) turbulence by neural networks is difficult because 3D turbulence is highly-nonlinear with high degrees of freedom and the corresponding simulation i…
Temporally sparse data assimilation for the small-scale reconstruction of turbulence
Yunpeng Wang, Zelong Yuan, Chenyue Xie +1
Previous works have shown that the small-scale information of incompressible homogeneous isotropic turbulence (HIT) is fully recoverable as long as sufficient large-scale structure…
Attention-Enhanced Neural Network Models for Turbulence Simulation
Wenhui Peng, Zelong Yuan, Jianchun Wang
Deep neural network models have shown a great potential in accelerating the simulation of fluid dynamic systems. Once trained, these models can make inference within seconds, thus…
Deconvolutional artificial neural network models for large eddy simulation of turbulence
Zelong Yuan, Chenyue Xie, Jianchun Wang
Deconvolutional artificial neural network (DANN) models are developed for subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence. The filtered velocities at differ…