Early warning indicators via latent stochastic dynamical systems

arXiv:2309.03842v3 Announce Type: replace-cross
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