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Early warning indicators via latent stochastic dynamical systems
April 8, 2024, 4:43 a.m. | Lingyu Feng, Ting Gao, Wang Xiao, Jinqiao Duan
cs.LG updates on arXiv.org arxiv.org
Abstract: Detecting early warning indicators for abrupt dynamical transitions in complex systems or high-dimensional observation data is essential in many real-world applications, such as brain diseases, natural disasters, and engineering reliability. To this end, we develop a novel approach: the directed anisotropic diffusion map that captures the latent evolutionary dynamics in the low-dimensional manifold. Then three effective warning signals (Onsager-Machlup Indicator, Sample Entropy Indicator, and Transition Probability Indicator) are derived through the latent coordinates and the …
abstract applications arxiv brain complex systems cs.lg data diffusion diseases engineering map natural natural disasters novel observation reliability stat.ml stochastic systems transitions type via world
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