Spatiotemporal Dynamical System

Spatiotemporal dynamical systems research focuses on modeling and predicting the evolution of systems changing over both space and time, often governed by partial differential equations. Current efforts concentrate on developing data-driven models, employing architectures like Fourier neural operators, neural differential equations, and transformers, often incorporating techniques like dimensionality reduction and attention mechanisms to handle high-dimensional data and improve computational efficiency. These advancements are crucial for improving predictions in diverse fields, ranging from fluid dynamics and climate modeling to urban planning and traffic flow optimization, enabling better understanding and control of complex real-world phenomena.