Statistical mechanics of complex systems for pattern identification

Shalabh Gupta, Asok Ray

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

This paper presents a statistical mechanics concept for identification of behavioral patterns in complex systems based on measurements (e.g., time series data) of macroscopically observable parameters and their operational characteristics. The tools of statistical mechanics, which provide a link between the microscopic (i.e., detailed) and macroscopic (i.e., aggregated) properties of a complex system are used to capture the emerging information and to identify the quasi-stationary evolution of behavioral patterns. The underlying theory is built upon thermodynamic formalism of symbol sequences in the setting of a generalized Ising model (GIM) of lattice-spin systems. In this context, transfer matrix analysis facilitates construction of pattern vectors from observed sequences. The proposed concept is experimentally validated on a richly instrumented laboratory apparatus that is operated under oscillating load for identification of evolving microstructural changes in polycrystalline alloys.

Original languageEnglish (US)
Pages (from-to)337-364
Number of pages28
JournalJournal of Statistical Physics
Volume134
Issue number2
DOIs
StatePublished - Jan 1 2009

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complex systems
statistical mechanics
Statistical Mechanics
Complex Systems
Thermodynamic Formalism
Matrix Analysis
Lattice System
Spin Systems
Transfer Matrix
Time Series Data
Ising model
Ising Model
emerging
time measurement
formalism
thermodynamics
Concepts

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Mathematical Physics

Cite this

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Statistical mechanics of complex systems for pattern identification. / Gupta, Shalabh; Ray, Asok.

In: Journal of Statistical Physics, Vol. 134, No. 2, 01.01.2009, p. 337-364.

Research output: Contribution to journalArticle

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