Year: 2014
Type: article
Abstract: Networks have become a popular way to concisely represent complex nonlinear systems where the interactions and parameters are imprecisely known. One challenge is how best to describe the associated dynamics, which can exhibit complicated behavior sensitive to small changes in parameters. A recently developed computational approach that we refer to as a database for dynamics provides a robust and mathematically rigorous description of global dynamics over large ranges of parameter space. To demonstrate the potential of this approach we consider two classical age-structured population models that share the same network diagram and have a similar nonlinear overcompensatory term, but nevertheless yield different patterns of qualitative behavior as a function of parameters. Using a generalization of these models we relate the different structure of the dynamics that are observed in the context of biologically relevant questions such as stable oscillations in populations, bistability, and permanence.
Source: Entropy
DOI: 10.3390/e16063379
Authors: Justin Bush, Konstantin Mischaikow
Institution: Rutgers, The State University of New Jersey
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: CDI-Type II: Collaborative Research: Computational Homology, Jamming, and Force Chains in Dense Granular Flows, Collaborative proposal: Computing Dynamics of Multiparameter Systems, CDI-TYPE II--COLLABORATIVE RESEARCH: Using Algebraic Topology to Connect Models with Measurements in Complex Nonequilibrium Systems
OpenAlex ID: W2012509944