Coarse Graining with Neural Operators for Simulating Chaotic Systems

Understand

Accurately predicting the long-term behavior of chaotic systems is crucial for various applications such as climate modeling.

• However, achieving such predictions typically requires iterative computations over a dense spatiotemporal grid to account for the unstable nature of chaotic systems, which is expensive and impractical in many real-world situations.
• An alternative approach to such a full-resolved simulation is using a coarse grid and then correcting its errors through a \textit{closure model}, which approximates the overall information from fine scales not captured in the coarse-grid simulation.
• Recently, ML approaches have been used for closure modeling, but they typically require a large number of training samples from expensive fully-resolved simulations (FRS).