3 citations · 8 across the 12 of their papers we have counts for
13 papers
ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation
Yunhan Xu, Qifeng Wu, Xunjin Li +10
Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geometry, and production-grade B-R…
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…