ArticleEntropy (Basel, Switzerland)2021
Information Geometric Theory in the Prediction of Abrupt Changes in System Dynamics.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Signatures of Extreme Events in Cumulative Entropic Spectrum.Entropy (Basel, Switzerland) · 2025Article
- Minimum Information Variability in Linear Langevin Systems via Model Predictive Control.Entropy (Basel, Switzerland) · 2024Article
- Information Geometry Theoretic Measures for Characterizing Neural Information Processing from Simulated EEG Signals.Entropy (Basel, Switzerland) · 2024Article
- Stochastic Dynamics of Fusion Low-to-High Confinement Mode (L-H) Transition: Correlation and Causal Analyses Using Information Geometry.Entropy (Basel, Switzerland) · 2023Article
- Monte Carlo Simulation of Stochastic Differential Equation to Study Information Geometry.Entropy (Basel, Switzerland) · 2022Article
- Information Geometry, Fluctuations, Non-Equilibrium Thermodynamics, and Geodesics in Complex Systems.Entropy (Basel, Switzerland) · 2021Review
- Causal Information Rate.Entropy (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors.
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Abstract
Detection and measurement of abrupt changes in a process can provide us with important tools for decision making in systems management. In particular, it can be utilised to predict the onset of a sudden event such as a rare, extreme event which causes the abrupt dynamical change in the system. Here, we investigate the prediction capability of information theory by focusing on how sensitive information-geometric theory (information length diagnostics) and entropy-based information theoretical method (information flow) are to abrupt changes. To this end, we utilise a non-autonomous Kramer equation by including a sudden perturbation to the system to mimic the onset of a sudden event and calculate time-dependent probability density functions (PDFs) and various statistical quantities with the help of numerical simulations. We show that information length diagnostics predict the onset of a sudden event better than the information flow. Furthermore, it is explicitly shown that the information flow like any other entropy-based measures has limitations in measuring perturbations which do not affect entropy.
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