Graph Neural Networks (GNNs) are useful for solving differential equations due to their ability to effectively model complex systems. Their flexibility allows them to handle diverse data types, sizes, and structures.
GNNs can learn from physical data, improving accuracy and enabling efficient approximations of PDE/ODEs. They mostly can generalize across different systems and domains. Furthermore, GNNs enhance traditional methods by providing hybrid models and error corrections, making them an interesting approach for advancing scientific and engineering research.