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Related Experiment Video

Updated: May 22, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Random dynamical models from time series.

Y I Molkov¹, E M Loskutov, D N Mukhin

¹Indiana University - Purdue University, Indianapolis, Indiana, USA. ymolkov@iupui.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|May 17, 2012

Summary

This study introduces a Bayesian method using artificial neural networks to model random dynamical systems from time series data. The approach accurately reproduces system behavior and predicts future changes, demonstrating its effectiveness on complex noise models.

Related Experiment Videos

Last Updated: May 22, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Area of Science:

Dynamical Systems
Machine Learning
Bayesian Inference

Background:

Stochastic dynamical systems are prevalent in various scientific fields.
Modeling these systems often involves complex time series data.
Traditional methods may struggle with non-Gaussian noise and non-autonomous behavior.

Purpose of the Study:

To develop a consistent Bayesian framework for modeling stochastic dynamical systems using time series.
To implement this framework using artificial neural networks.
To demonstrate the model's capability in reproducing stationary behavior and predicting qualitative changes.

Main Methods:

Formulation of a Bayesian approach for stochastic dynamical systems.
Implementation via artificial neural networks.
Validation on model examples including discrete maps and flow systems with Langevin force.

Main Results:

The proposed Bayesian method successfully models stochastic dynamical systems.
The approach accurately reproduces observed stationary regimes.
The method demonstrates predictive power for qualitative behavioral changes in weakly non-autonomous systems.

Conclusions:

The Bayesian approach with artificial neural networks provides a robust method for modeling stochastic dynamical systems.
This technique is effective for both reproducing current behavior and predicting future dynamics.
The approach is validated on diverse systems with complex noise characteristics.