Feb. 12, 2024, 5:43 a.m. | Thomas M. Bury Daniel Dylewsky Chris T. Bauch Madhur Anand Leon Glass Alvin Shrier Gil Bub

cs.LG updates on arXiv.org arxiv.org

Many natural and man-made systems are prone to critical transitions -- abrupt and potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal (EWS) for critical transitions by learning generic features of bifurcations (dynamical instabilities) from large simulated training data sets. So far, classifiers have only been trained to predict continuous-time bifurcations, ignoring rich dynamics unique to discrete-time bifurcations. Here, we train a deep learning classifier to provide an EWS for the five local discrete-time bifurcations …

classifiers cs.lg data data sets deep learning dynamics features math.ds natural q-bio.qm signal systems training training data transitions

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