54 citations · 163 across the 20 of their papers we have counts for
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Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects
Weiwei Zhang, Wei Suo, Jiahao Song +1
In recent years, Physics-Informed Neural Networks (PINNs) have become a representative method for solving partial differential equations (PDEs) with neural networks. PINNs provide…
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…