New Paper from Veronica Ciocanel: Model Reconstruction from Temporal Data for Coupled Oscillator Networks

November 22, 2019

New Paper from Veronica Ciocanel: Model Reconstruction from Temporal Data for Coupled Oscillator Networks

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Description

The paper Model Reconstruction from Temporal Data for Coupled Oscillator Networks by MBI postdoc Veronica Ciocanel and collaborators was recently accepted to appear in the journal Chaos. Veronica describes the paper as follows:

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Photo of Veronica Ciocanel

In this work, we explore models of complex systems where interactions between individual agents can lead to collective behavior such as synchronization. Examples of applications include neurons in the brain, generators in a power grid, or individuals in a crowd, where interactions can cause unexpected behaviors to emerge. In particular, we consider the Kuramoto model of coupled oscillators and explore a method of reconstructing the interaction network and the oscillator dynamics from time series observation data. The proposed method of reconstruction uses techniques commonly used for training artificial neural network. This inference is particularly challenging when the oscillators quickly synchronize, therefore we explore perturbations that provide additional transient data for reconstruction.

View the paper: https://go.osu.edu/BhKJ

Chaos 29, 103116 (2019)