Spatiotemporal Dynamic

Spatiotemporal dynamics research focuses on understanding and modeling systems that evolve over both space and time. Current efforts concentrate on developing accurate and efficient predictive models, often employing graph neural networks (GNNs), Fourier neural operators (FNOs), and physics-informed neural networks (PINNs) to capture complex interactions and incorporate prior physical knowledge. These advancements are significantly impacting fields like fluid dynamics, weather forecasting, and urban planning by enabling more accurate simulations and predictions of complex systems with improved computational efficiency. The development of robust and theoretically grounded methods for handling missing data and irregular spatial sampling remains a key challenge.