1 citations · 1 across the 5 of their papers we have counts for
5 papers
Deep learning accelerated solutions of incompressible Navier-Stokes equations on non-uniform Cartesian grids
Heming Bai, Dong Zhang, Shengze Cai +1
In incompressible flow simulations, non-uniform grids efficiently capture localized flow features; however, their spatially varying resolutions severely exacerbate computational co…
Hybrid deep learning and iterative methods for accelerated solutions of viscous incompressible flow
Heming Bai, Xin Bian
The pressure Poisson equation, central to the fractional step method in incompressible flow simulations, incurs high computational costs due to the iterative solution of large-scal…
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…
Data-driven modeling of unsteady flow based on deep operator network
Heming Bai, Zhicheng Wang, Xuesen Chu +2
Time-dependent flow fields are typically generated by a computational fluid dynamics (CFD) method, which is an extremely time-consuming process. However, the latent relationship be…
Self-supervised learning based on Transformer for flow reconstruction and prediction
Bonan Xu, Yuanye Zhou, Xin Bian
Machine learning has great potential for efficient reconstruction and prediction of flow fields. However, existing datasets may have highly diversified labels for different flow sc…