Mesh Based

Mesh-based methods are increasingly used to model and simulate complex physical systems, primarily focusing on efficiently solving partial differential equations (PDEs) and enabling large-scale simulations. Current research emphasizes developing scalable and consistent graph neural networks (GNNs), often incorporating hierarchical message passing or U-Net architectures, to handle the inherent complexities of mesh data, particularly in high-performance computing environments. These advancements are improving the accuracy and efficiency of simulations across diverse fields, including fluid dynamics, material science, and medical image analysis, leading to more realistic and computationally feasible models for various applications.