12 papers
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…
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…
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…