most citedAn unstructured adaptive mesh refinement for steady flows based on physics-informed neural networks

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

physics.flu-dyn2025

Physics-informed neural networks for hidden boundary detection and flow field reconstruction

Yongzheng Zhu, Weizheng Chen, Jian Deng +1

Simultaneously detecting hidden solid boundaries and reconstructing flow fields from sparse observations poses a significant inverse challenge in fluid mechanics. This study presen…

physics.flu-dyn2024★ 1 cited

An unstructured adaptive mesh refinement for steady flows based on physics-informed neural networks

Yongzheng Zhu, Shiji Zhao, Yuanye Zhou +2

Mesh generation is essential for accurate and efficient computational fluid dynamics simulations. To resolve critical features in the flow, adaptive mesh refinement (AMR) is routin…