Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems
This addresses the problem of predicting rare or unseen behaviors in complex systems for researchers in physics and computational science, though it appears incremental as it builds on existing emulator methods.
The paper tackled forecasting out-of-training dynamics in chaotic systems using a deep learning emulator, demonstrating its ability to predict phenomena like spontaneous relaminarisation and zero-shot parameter extrapolation in Kuramoto-Sivashinsky and beta-plane turbulence models.
We demonstrate that a deep learning emulator for chaotic systems can forecast phenomena absent from training data. Using the Kuramoto-Sivashinsky and beta-plane turbulence models, we evaluate the emulator through scenarios probing the fundamental phenomena of both systems: forecasting spontaneous relaminarisation, capturing initialisation of arbitrary chaotic states, zero-shot prediction of dynamics with parameter values outside of the training range, and characterisation of dynamical statistics from artificially restricted training datasets. Our results show that deep learning emulators can uncover emergent behaviours and rare events in complex systems by learning underlying mathematical rules, rather than merely mimicking observed patterns.