<< ️(AA) 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 (CNN) to identify precursors to extreme events. >>
<< ️(They) 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 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. >>
Rahul Agarwal, Mustafa A. Mohamad. Extreme Event Precursor Prediction in Turbulent Dynamical Systems via CNN-Augmented Recurrence Analysis. arXiv: 2508.04301v1 [cs.CE]. Aug 6, 2025.
Also: turbulence, chaos, in https://www.inkgmr.net/kwrds.html
Keywords: gst, turbulence, chaos, extreme events, convolutional neural networks (CNN).