Extreme Event Precursor Prediction in Turbulent Dynamical Systems via CNN-Augmented Recurrence Analysis

Abstract

We present a general framework to predict precursors to extreme events in turbulent dynamical systems. The approach combines phase-space reconstruction techniques with recurrence matrices and convolutional neural networks to identify precursors to extreme events. We evaluate the framework across three distinct testbed systems: a triad turbulent interaction model, a prototype stochastic anisotropic turbulent flow, and the Kolmogorov flow. This method offers three key advantages: (1) a threshold-free classification strategy that eliminates subjective parameter tuning, (2) efficient training using only $\mathcal{O}(100)$ recurrence matrices, and (3) ability to generalize to unseen systems. The results demonstrate robust predictive performance across all test systems: 96\% detection rate for the triad model with a mean lead time of 1.8 time units, 96\% for the anisotropic turbulent flow with a mean lead time of 6.1 time units, and 93\% for the Kolmogorov flow with a mean lead time of 22.7 units.