Statistical Theory and Modeling of Rare, Extreme Events: Entropy and Information Theory
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Statistical Physics".
Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 11614
Special Issue Editor
Interests: fluid dynamics; magnetohydrodynamics (MHD); plasma physics; self-organisation; non-equilibrium statistical mechanics; turbulence; solar/stellar physics; magnetic fusion; information theory; homeostasis in biosystems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Rare, extreme events refer to those that occur rarely but have a significant impact when they occur. Examples include the sudden outbreak of devastating infectious diseases, solar flares, extreme weather conditions, flood, forest fire, etc. Such events often result from strong non-linear interactions across a wide range of different lengths and time scales, with the limited utility of conventional perturbative methods. Furthermore, rare events contribute to tails of a probabilistic distribution function while hardly affecting mean values or variance. These present the main challenge in the theory and modeling of rare, extreme events.
This Special Issue aims to present different approaches to address this challenge in a broad range of disciplines from the perspective of entropy and information theory. This will include the development of a new theory, applications to phenomena in nature, industry, society, medicine, and finances, as well as data analysis from these systems.
Dr. Eun-jin Kim
Guest Editor
Manuscript Submission Information
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Keywords
- Rare extreme event
- Entropy
- Information theory
- Transfer entropy
- Wavelet entropy
- Multiscale entropy
- Statistical physics
- Large deviation theory
- Extreme value theory
- Probability distribution
- Extreme principle
- Entropy production
- Turbulence
- Quantum systems
- Self-organization
- Avalanches
- Geometric frustration
- Machine learning
- Neural network
- Deep learning
- Spin systems
- Magnetization