Extreme Event Precursor Prediction in Turbulent Dynamical Systems via CNN-Augmented Recurrence Analysis
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908480182419456 |
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| author | Agarwal, Rahul Mohamad, Mustafa A. |
| author_facet | Agarwal, Rahul Mohamad, Mustafa A. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_04301 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extreme Event Precursor Prediction in Turbulent Dynamical Systems via CNN-Augmented Recurrence Analysis Agarwal, Rahul Mohamad, Mustafa A. Computational Engineering, Finance, and Science Dynamical Systems Chaotic Dynamics 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. |
| title | Extreme Event Precursor Prediction in Turbulent Dynamical Systems via CNN-Augmented Recurrence Analysis |
| topic | Computational Engineering, Finance, and Science Dynamical Systems Chaotic Dynamics |
| url | https://arxiv.org/abs/2508.04301 |