A scalable algorithm to order and annotate continuous observations reveals the metastable states visited by dynamical systems

TitleA scalable algorithm to order and annotate continuous observations reveals the metastable states visited by dynamical systems
Publication TypeJournal Article
Year of Publication2013
AuthorsBlöchliger N., Vitalis A., Caflisch A.
JournalComputer Physics Communications
Volume184
Issue11
Pagination2446 - 2453
Date PublishedNov 2013
Type of ArticleResearch Article
KeywordsComplex system, Free energy basins, Minimum spanning tree, Scalable algorithm, Trajectory analysis
Abstract

Advances in IT infrastructure have enabled the generation and storage of very large data sets describing complex systems continuously in time. These can derive from both simulations and measurements. Analysis of such data requires the availability of scalable algorithms. In this contribution, we propose a scalable algorithm that partitions instantaneous observations (snapshots) of a complex system into kinetically distinct sets (termed basins). To do so, we use a combination of ordering snapshots employing the method’s only essential parameter, i.e., a definition of pairwise distance, and annotating the resultant sequence, the so-called progress index, in different ways. Specifically, we propose a combination of cut-based and structural annotations with the former responsible for the kinetic grouping and the latter for diagnostics and interpretation. The method is applied to an illustrative test case, and the scaling of an approximate version is demonstrated to be O(N log N) with N being the number of snapshots. Two real-world data sets from river hydrology measurements and protein folding simulations are then used to highlight the utility of the method in finding basins for complex systems. Both limitations and benefits of the approach are discussed along with routes for future research.

URLhttp://www.sciencedirect.com/science/article/pii/S0010465513002038
DOI10.1016/j.cpc.2013.06.009
pubindex

0177

Alternate JournalComput. Phys. Commun.