Articles written in Indian Academy of Sciences Conference Series
Volume 1 Issue 1 December 2017 pp 25-34 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016
Data assimilation refers to a set of techniques used to combine observational information with numerical models for chaotic dynamical systems and provides a rich interface between dynamical and statistical methodologies in nonlinear dynamics. The main aim of this paper is to compare and contrast two extensively studied paradigms in each of these approaches: on one hand, the ensemble Kalman filter which is a statistical estimation technique, and on the other hand, chaotic synchronization that has been studied in many other contexts, by viewing synchronization as a data assimilation method. In particular, we study the sensitivity of these two methods to changes in observational noise and observational frequency, using both simulated observations and data obtained from an experimental realization of a commonly used low-dimensional dynamical system, namely, Chua circuit, in both the periodic as well as the chaotic regime.