Huisinga, W. and Schütte, Ch. and Stuart, A. M. (2003) Extracting Macroscopic Stochastic Dynamics: Model Problems. Comm. Pure Appl. Math., 56 (2). pp. 234269.

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Official URL: http://dx.doi.org/10.1002/cpa.10057
Abstract
The purpose of this work is to shed light on an algorithm designed to extract effective macroscopic models from detailed microscopic simulations. The particular algorithm we study is a recently developed transfer operator approach due to Schütte et al. [20]. The investigations involve the formulation, and subsequent numerical study, of a class of model problems. The model problems are ordinary differential equations constructed to have the property that, when projected onto a lowdimensional subspace, the dynamics is approximately that of a stochastic differential equation exhibiting a finitestatespace Markov chain structure. The numerical studies show that the transfer operator approach can accurately extract finitestate Markov chain behavior embedded within highdimensional ordinary differential equations. In so doing the studies lend considerable weight to existing applications of the algorithm to the complex systems arising in applications such as molecular dynamics. The algorithm is predicated on the assumption of Markovian input data; further numerical studies probe the role of memory effects. Although preliminary, these studies of memory indicate interesting avenues for further development of the transfer operator methodology.
Item Type:  Article 

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 > BioComputing Group 
ID Code:  72 
Deposited By:  Admin Administrator 
Deposited On:  03 Jan 2009 20:20 
Last Modified:  03 Mar 2017 14:39 
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