Unstructured Grid

Unstructured grids, irregular mesh structures representing complex geometries, pose significant challenges for solving partial differential equations (PDEs) in various scientific domains. Current research focuses on leveraging deep learning, particularly graph neural networks (GNNs) and convolutional neural networks (CNNs), often combined with techniques like multigrid methods and autoencoders, to efficiently solve PDEs on these grids and improve data processing. These advancements are crucial for enhancing accuracy and computational efficiency in fields like computational fluid dynamics, meteorology, and material science, enabling more realistic simulations and improved predictions.