ArticlePloS one2015
Behavior of Early Warnings near the Critical Temperature in the Two-Dimensional Ising Model.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Modular tipping points: How local network structure impacts critical transitions in networked spin systems.PloS one · 2023Article
- Critical transitions in degree mixed networks: A discovery of forbidden tipping regions in networked spin systems.PloS one · 2022Article
- Transition prediction in the Ising-model.PloS one · 2021Article
- Analysis of properties of Ising and Kuramoto models that are preserved in networks constructed by visualization algorithms.PloS one · 2019Article
- The Role of the Autonomic Nervous System on Cardiac Rhythm during the Evolution of Diabetes Mellitus Using Heart Rate Variability as a Biomarker.Journal of diabetes research · 2019Article
- Assessing sustainability in North America's ecosystems using criticality and information theory.PloS one · 2018Article
- Article
- Multifractality and Network Analysis of Phase Transition.PloS one · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Among the properties that are common to complex systems, the presence of critical thresholds in the dynamics of the system is one of the most important. Recently, there has been interest in the universalities that occur in the behavior of systems near critical points. These universal properties make it possible to estimate how far a system is from a critical threshold. Several early-warning signals have been reported in time series representing systems near catastrophic shifts. The proper understanding of these early-warnings may allow the prediction and perhaps control of these dramatic shifts in a wide variety of systems. In this paper we analyze this universal behavior for a system that is a paradigm of phase transitions, the Ising model. We study the behavior of the early-warning signals and the way the temporal correlations of the system increase when the system is near the critical point.
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Registered trials
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