54 citations · 107 across the 15 of their papers we have counts for
6 papers · 1 filter
Discovery of knowledge of wall-bounded turbulence via symbolic regression
ZhongXin Yang, XiangLin Shan, WeiWei Zhang
With the development of high performance computer and experimental technology, the study of turbulence has accumulated a large number of high fidelity data. However, few general tu…
An analysis and solution of ill-conditioning in physics-informed neural networks
Wenbo Cao, Weiwei Zhang
Physics-informed neural networks (PINNs) have recently emerged as a novel and popular approach for solving forward and inverse problems involving partial differential equations (PD…
New Interpretation for error propagation of data-driven Reynolds stress closures via global stability analysis
Xianglin Shan, Wenbo Cao, Weiwei Zhang
In light of the challenges surrounding convergence and error propagation encountered in Reynolds-averaged Navier-Stokes (RANS) equations with data-driven Reynolds stress closures,…
A solver for subsonic flow around airfoils based on physics-informed neural networks and mesh transformation
Wenbo Cao, Jiahao Song, Weiwei Zhang
Physics-informed neural networks (PINNs) have recently become a new popular method for solving forward and inverse problems governed by partial differential equations (PDEs). Howev…
A complete state-space solution model for inviscid flow around airfoils based on physics-informed neural networks
Wenbo Cao, Jiahao Song, Weiwei Zhang
Engineering problems often involve solving partial differential equations (PDEs) over a range of similar problem setups with various state parameters. In traditional numerical meth…
VW-PINNs: A volume weighting method for PDE residuals in physics-informed neural networks
Jiahao Song, Wenbo Cao, Fei Liao +1
Physics-informed neural networks (PINNs) have shown remarkable prospects in the solving the forward and inverse problems involving partial differential equations (PDEs). The method…