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Updated: Jun 17, 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

Robust extremes in chaotic deterministic systems.

Renato Vitolo¹, Mark P Holland, Christopher A T Ferro

¹School of Engineering, Computing and Mathematics, University of Exeter, Exeter, Devon EX4 4QF, United Kingdom. r.vitolo@exeter.ac.uk

Chaos (Woodbury, N.Y.)

|January 12, 2010

Summary

This summary is machine-generated.

This study introduces robust extremes in chaotic systems, showing extreme value statistics smoothly depend on control parameters. This finding enhances extreme value distribution estimation and prediction in nonstationary systems.

Related Experiment Videos

Last Updated: Jun 17, 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:

Chaos theory
Statistical modeling
Dynamical systems

Background:

Deterministic chaotic systems often exhibit unpredictable extreme events.
Understanding the stability of extreme value statistics is crucial for reliable predictions.

Purpose of the Study:

Introduce the concept of robust extremes in deterministic chaotic systems.
Develop theoretical results and inferential techniques for robust extremes.
Illustrate and prove robustness for specific chaotic models.

Main Methods:

Numerical simulations using the Lorenz model.
Analytical proofs for one-dimensional Lorenz maps.
Formulation of conditions for robust extremes with explicit observables.

Main Results:

Demonstrated numerical evidence of robust extremes in the Lorenz system.
Provided analytical proof of robustness for specific Lorenz maps and observables.
Established conditions for guaranteeing robust extremes.

Conclusions:

Robust extremes offer a framework for more precise statistical estimation of extreme value distributions.
The concept aids in predicting future extremes in nonstationary systems, as shown with extreme wind speeds in a quasigeostrophic model.