Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces

Understand

Tipping points are abrupt, drastic, and often irreversible changes in the evolution of non-stationary and chaotic dynamical systems.

• For instance, increased greenhouse gas concentrations are predicted to lead to drastic decreases in low cloud cover, referred to as a climatological tipping point.
• In this paper, we learn the evolution of such non-stationary dynamical systems using a novel recurrent neural operator (RNO), which learns mappings between function spaces.
• After training RNO on only the pre-tipping dynamics, we employ it to detect future tipping points using an uncertainty-based approach.