Divide And Conquer: Learning Chaotic Dynamical Systems With Multistep Penalty Neural Ordinary Differential Equations

Understand

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering.

• Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems.
• However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems.
• In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems.