4 citations · 5 across the 4 of their papers we have counts for
5 papers
Cova-PINN: Cross-Domain Conservation Physics-Informed Neural Network for Fluid-Solid Conjugate Heat Transfer in Complex Geometries
Weizheng Zhang, Xunjie Xie, Hao Pan +1
Multi-domain physics-informed neural networks (PINNs) flexibly model medium-specific representations to solve fluid--solid conjugate heat transfer (CHT). However, standard multi-do…
MUSA-PINN: Multi-scale Weak-form Physics-Informed Neural Networks for Fluid Flow in Complex Geometries
Weizheng Zhang, Xunjie Xie, Hao Pan +4
While Physics-Informed Neural Networks (PINNs) offer a mesh-free approach to solving fluid-flow PDEs, standard point-wise residual minimization suffers from convergence pathologies…
DualMS: Implicit Dual-Channel Minimal Surface Optimization for Heat Exchanger Design
Weizheng Zhang, Hao Pan, Lin Lu +4
Heat exchangers are critical components in a wide range of engineering applications, from energy systems to chemical processing, where efficient thermal management is essential. Th…
PS-CAD: Local Geometry Guidance via Prompting and Selection for CAD Reconstruction
Bingchen Yang, Haiyong Jiang, Hao Pan +3
Reverse engineering CAD models from raw geometry is a classic but challenging research problem. In particular, reconstructing the CAD modeling sequence from point clouds provides g…
iPUNet:Iterative Cross Field Guided Point Cloud Upsampling
Guangshun Wei, Hao Pan, Shaojie Zhuang +2
Point clouds acquired by 3D scanning devices are often sparse, noisy, and non-uniform, causing a loss of geometric features. To facilitate the usability of point clouds in downstre…