Horenko, I. and Hartmann, C. and Schütte, Ch. and Noé, F.
(2007)
*Data-based Parameter Estimation of Generalized Multidimensional Langevin Processes.*
Phys. Rev. E, 76
(01).
016706.

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Official URL: http://dx.doi.org/10.1103/PhysRevE.76.016706

## 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.

Item Type: | Article |
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Subjects: | Mathematical and Computer Sciences > Mathematics |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics Department of Mathematics and Computer Science > Institute of Mathematics > Comp. Molecular Biology Department of Mathematics and Computer Science > Institute of Mathematics > Cellular Mechanics Group Department of Mathematics and Computer Science > Institute of Mathematics > BioComputing Group |

ID Code: | 31 |

Deposited By: | Admin Administrator |

Deposited On: | 03 Jan 2009 20:20 |

Last Modified: | 03 Mar 2017 14:39 |

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