5 papers
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…
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…