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

The paper defines the event/precursor framework and reports strong numerical results, but key implementation details are under-specified (e.g., ε selection for recurrence plots, τ_max and threshold n choices, windowing, and CNN architecture/training protocol), while the text also touts “threshold-free” classification even though Algorithm 1 and event definition use explicit thresholds. The model solution reproduces the paper’s Definitions and Algorithm 1 faithfully and sets a reproducible pipeline with sensible defaults, but it omits the paper’s central CNN step and provides no quantitative validation. Hence, the paper’s argument is promising yet underspecified for full reproducibility, and the model’s solution is methodologically aligned but incomplete in execution. Evidence: Algorithm 1 and the Fb/K labeling criteria are stated verbatim in the PDF, as are the event and precursor definitions and reported detection/lead-time statistics for the three benchmarks ; the paper also emphasizes a “threshold-free classification” in conclusions despite using explicit thresholds elsewhere .