TY - GEN
T1 - Coarse collective dynamics of animal groups
AU - Frewen, Thomas A.
AU - Couzin, Iain D.
AU - Kolpas, Allison
AU - Moehlis, Jeff
AU - Coifman, Ronald
AU - Kevrekidis, Ioannis G.
N1 - Funding Information:
This work was partially supported by the National Science Foundation and by the US AFOSR.
PY - 2011
Y1 - 2011
N2 - The coarse-grained, computer-assisted analysis of models of collective dynamics in animal groups involves (a) identifying appropriate observables that best describe the state of these complex systems and (b) characterizing the dynamics of such observables. We devise "equation-free" simulation protocols for the analysis of a prototypical individual-based model of collective group dynamics. Our approach allows the extraction of information at the macroscopic level via parsimonious usage of the detailed, "microscopic" computational model. Identification of meaningful coarse observables ("reduction coordinates") is critical to the success of such an approach, and we use a recently-developed dimensionality-reduction approach (diffusion maps) to detect good observables based on data generated by local model simulation bursts. This approach can be more generally applicable to the study of coherent behavior in a broad class of collective systems (e.g., collective cell migration).
AB - The coarse-grained, computer-assisted analysis of models of collective dynamics in animal groups involves (a) identifying appropriate observables that best describe the state of these complex systems and (b) characterizing the dynamics of such observables. We devise "equation-free" simulation protocols for the analysis of a prototypical individual-based model of collective group dynamics. Our approach allows the extraction of information at the macroscopic level via parsimonious usage of the detailed, "microscopic" computational model. Identification of meaningful coarse observables ("reduction coordinates") is critical to the success of such an approach, and we use a recently-developed dimensionality-reduction approach (diffusion maps) to detect good observables based on data generated by local model simulation bursts. This approach can be more generally applicable to the study of coherent behavior in a broad class of collective systems (e.g., collective cell migration).
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U2 - 10.1007/978-3-642-14941-2_16
DO - 10.1007/978-3-642-14941-2_16
M3 - Conference contribution
AN - SCOPUS:78651523933
SN - 9783642149405
T3 - Lecture Notes in Computational Science and Engineering
SP - 299
EP - 309
BT - Coping with Complexity
T2 - International Research Workshop: Coping with Complexity: Model Reduction and Data Analysis
Y2 - 31 August 2009 through 4 September 2009
ER -