1 citations · 2 across the 2 of their papers we have counts for
2 papers
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…
physics.flu-dyn2023★ 1 cited
Continuous and discontinous compressible flows in a converging-diverging channel solved by physics-informed neural networks without data
Liang Hong, Song Zilong, Zhao Chong +1
Physics-informed neural networks (PINNs) are employed to solve the classical compressible flow problem in a converging-diverging nozzle. This problem represents a typical example d…