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A unifying view of synchronization for data assimilation in complex nonlinear networks
Henry D I Abarbanel1, Sasha Shirman2, Daniel Breen2
1Department of Physics and Marine Physical Laboratory (Scripps Institution of Oceanography), University of California, San Diego, 9500 Gilman Drive, Mail Code 0374, La Jolla, California 92093-0374, USA.
Chaos (Woodbury, N.Y.)
|January 1, 2018
Summary
This study introduces statistical data assimilation to estimate unknown parameters and connectivity in nonlinear systems. This framework improves model accuracy for predicting network behavior and understanding complex dynamical processes.
Area of Science:
- Complex Systems
- Network Science
- Computational Modeling
Background:
- Networks of nonlinear systems often possess unobservable parameters and dynamics.
- Estimating network properties and states from limited, noisy measurements is challenging.
Purpose of the Study:
- To present a general framework for solving the inverse problem of determining model properties from observed data.
- To enable accurate state estimation and model validation for nonlinear systems.
Main Methods:
- Statistical data assimilation framework is described.
- Utilizes conditional probability distributions of model state variables based on observations.
Main Results:
- The framework allows for the estimation of network connectivity and model states.
- Enables validation of models against new data and prediction of future behavior.
Conclusions:
- Statistical data assimilation provides a robust method for analyzing complex nonlinear networks.
- Applicable to diverse fields like weather prediction, electrical engineering, and neuroscience.
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