Data-based parameter estimation of generalized multidimensional Langevin processes.
Phys Rev E Stat Nonlin Soft Matter Phys
; 76(1 Pt 2): 016706, 2007 Jul.
Article
em En
| MEDLINE
| ID: mdl-17677593
ABSTRACT
The generalized Langevin equation is useful for modeling a wide range of physical processes. Unfortunately its parameters, especially the memory function, are difficult to determine for nontrivial processes. We establish relations between a time-discrete generalized Langevin model and discrete multivariate autoregressive (AR) or autoregressive moving average models (ARMA). This allows a wide range of discrete linear methods known from time series analysis to be applied. In particular, the determination of the memory function via the order of the respective AR or ARMA model is addressed. The method is illustrated on a one-dimensional test system and subsequently applied to the molecular dynamics time series of a biomolecule that exhibits an interesting relationship between the solvent method used, the respective molecular conformation, and the depth of the memory.
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Base de dados:
MEDLINE
Idioma:
En
Revista:
Phys Rev E Stat Nonlin Soft Matter Phys
Assunto da revista:
BIOFISICA
/
FISIOLOGIA
Ano de publicação:
2007
Tipo de documento:
Article
País de afiliação:
Alemanha