14 papers · 1 filter
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…
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…
Stable Fine-Time-Step Long-Horizon Turbulence Prediction with a Multi-Stepsize Mixture-of-Experts Neural Operator
Guanyu Pan, Huiyu Yang, Yunpeng Wang +3
Neural operators have been increasingly used as data-driven surrogates for time-marching predictions of turbulent flows. However, long-horizon autoregressive prediction is sensitiv…
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…
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…
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…