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Synchronization based system identification of an extended excitable system.

S Berg¹, S Luther, U Parlitz

¹Drittes Physikalisches Institut, Georg-August-Universität, Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany. berg@physik3.gwdg.de

Chaos (Woodbury, N.Y.)

|October 7, 2011

Summary

This summary is machine-generated.

This study introduces a method to estimate parameters in complex excitable systems using time series data. The technique synchronizes model equations to minimize errors, aiding in understanding spiral wave dynamics.

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Area of Science:

Computational Physics
Nonlinear Dynamics
Systems Biology

Background:

Excitable systems, such as those found in biological tissues or chemical reactions, exhibit complex spatio-temporal dynamics.
Understanding and modeling these dynamics, particularly phenomena like spiral waves, is crucial in various scientific fields.
Accurate parameter estimation is essential for reliable modeling and prediction of excitable system behavior.

Purpose of the Study:

To develop and demonstrate a basic state and parameter estimation scheme for extended excitable systems.
To utilize time series data from spatial sampling points to drive and synchronize model equations.
To estimate model parameters by minimizing the synchronization error.

Main Methods:

A novel parameter estimation scheme is presented.
Time series data from a spatial grid of sampling points are used.
Model equations are driven and synchronized using the time series data.
Synchronization error is minimized to estimate model parameters.
The scheme is implemented and tested on graphics processing units (GPUs).

Main Results:

The parameter estimation scheme successfully synchronizes model equations.
Synchronization error minimization effectively estimates model parameters.
The method is demonstrated on generic models of excitable media.
Spiral wave dynamics and chaotic spiral break-up were reproduced and analyzed.

Conclusions:

The presented estimation scheme provides a viable method for parameterizing extended excitable systems.
The approach is effective even for complex dynamics like chaotic spiral break-up.
GPU implementation allows for efficient computation and analysis of these complex systems.