3 citations · 3 across the 3 of their papers we have counts for
3 papers
physics.flu-dyn2025
FENN: Feature-enhanced neural network for solving partial differential equations involving fluid mechanics
Jiahao Song, Wenbo Cao, Weiwei Zhang
Physics-informed neural networks (PINNs) have shown remarkable prospects in solving forward and inverse problems involving partial differential equations (PDEs). However, PINNs sti…
physics.flu-dyn2025★ 3 cited
Solving all laminar flows around airfoils all-at-once using a parametric neural network solver
Wenbo Cao, Shixiang Tang, Qianhong Ma +2
Recent years have witnessed increasing research interests of physics-informed neural networks (PINNs) in solving forward, inverse, and parametric problems governed by partial diffe…
physics.flu-dyn2024
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,…