14 citations · 46 across the 17 of their papers we have counts for
18 papers
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…
A Geometry-Aware Triplane Field Network for Vehicle Aerodynamic Prediction
Kangkang Qi, Huiyu Yang, Keqi Ding +5
High-fidelity computational fluid dynamics (CFD) is crucial to vehicle aerodynamic analysis, but its cost still constrains early-stage design exploration. Machine-learning-based su…
RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics
Bojun Zhang, Huiyu Yang, Yunpeng Wang +4
Rapid aerodynamic evaluation is crucial for modern vehicle design, yet existing neural operators struggle to capture intricate spatial correlations. We propose the rotary-enhanced…
Large-eddy simulation nets (LESnets) based on physics-informed neural operator for wall-bounded turbulence
Sunan Zhao, Yunpeng Wang, Huiyu Yang +2
Accurate and efficient prediction of three-dimensional (3D) wall-bounded turbulent flows poses a significant challenge for machine learning methods, particularly in scenarios where…
Long-time turbulence prediction at fine time steps using multi-stride neural operators
Guanyu Pan, Huiyu Yang, Yunpeng Wang +3
Long-time turbulence prediction at fine time steps is costly and prone to error accumulation, while accurate short-term fields do not ensure accurate long-time statistics. We propo…
Physics-Informed Transformer operator for the prediction of three-dimensional turbulence
Zhihong Guo, Sunan Zhao, Huiyu Yang +2
Data-driven turbulence prediction methods often face challenges related to data dependency and lack of physical interpretability. In this paper, we propose a physics-informed Trans…
Integrating Fourier Neural Operator with Diffusion Model for Autoregressive Predictions of Three-dimensional Turbulence
Yuchi Jiang, Yunpeng Wang, Huiyu Yang +1
Accurately autoregressive prediction of three-dimensional (3D) turbulence has been one of the most challenging problems for machine learning approaches. Diffusion models have demon…
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…
Implicit factorized transformer approach to fast prediction of turbulent channel flows
Huiyu Yang, Yunpeng Wang, Jianchun Wang
Transformer neural operators have recently become an effective approach for surrogate modeling of systems governed by partial differential equations (PDEs). In this paper, we intro…
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…
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…
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…
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…