Physic Informed Graph

Physics-informed graph neural networks (PI-GNNs) integrate physical laws and principles into graph neural network architectures to improve the accuracy, efficiency, and data requirements of machine learning models for various applications. Current research focuses on incorporating physical constraints and symmetries within GNN frameworks like GraphSAGE and PointNet++, often employing techniques such as equivariant layers and Lagrangian methods. This approach is proving valuable across diverse fields, enhancing predictions in areas such as fluid dynamics, ice sheet modeling, and power grid analysis by leveraging prior knowledge to overcome limitations of data scarcity and model complexity.