2 citations · 2 across the 4 of their papers we have counts for
5 papers
Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations
Biao Chen, Jing Wang, Hairun Xie +4
Neural operators have emerged as powerful deep learning frameworks for approximating solution operators of parameterized partial differential equations (PDE). However, current meth…
LFR-PINO: A Layered Fourier Reduced Physics-Informed Neural Operator for Parametric PDEs
Jing Wang, Biao Chen, Hairun Xie +4
Physics-informed neural operators have emerged as a powerful paradigm for solving parametric partial differential equations (PDEs), particularly in the aerospace field, enabling th…
FuncGenFoil: Airfoil Generation and Editing Model in Function Space
Jinouwen Zhang, Junjie Ren, Qianhong Ma +9
Aircraft manufacturing is the jewel in the crown of industry, in which generating high-fidelity airfoil geometries with controllable and editable representations remains a fundamen…
DiffFluid: Plain Diffusion Models are Effective Predictors of Flow Dynamics
Dongyu Luo, Jianyu Wu, Jing Wang +3
We showcase the plain diffusion models with Transformers are effective predictors of fluid dynamics under various working conditions, e.g., Darcy flow and high Reynolds number. Unl…
AFBench: A Large-scale Benchmark for Airfoil Design
Jian Liu, Jianyu Wu, Hairun Xie +8
Data-driven generative models have emerged as promising approaches towards achieving efficient mechanical inverse design. However, due to prohibitively high cost in time and money,…