TY - JOUR
T1 - Statistical mechanics of complex systems for pattern identification
AU - Gupta, Shalabh
AU - Ray, Asok
N1 - Funding Information:
This work has been supported in part by the U.S. Army Research Office (ARO) under Grant No. W911NF-07-1-0376, by NASA under Cooperative Agreement No. NNX07AK49A, and by the U.S. Office of Naval Research (ONR) under Grant No. N00014-08-1-0380. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies.
PY - 2009/1
Y1 - 2009/1
N2 - 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.
AB - 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.
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U2 - 10.1007/s10955-009-9679-3
DO - 10.1007/s10955-009-9679-3
M3 - Article
AN - SCOPUS:60449116089
SN - 0022-4715
VL - 134
SP - 337
EP - 364
JO - Journal of Statistical Physics
JF - Journal of Statistical Physics
IS - 2
ER -