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Updated: Dec 26, 2025

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

Prediction in a driven-dissipative system displaying a continuous phase transition using machine learning.

Chon-Kit Pun¹, Sakib Matin¹, W Klein^1,2

¹Department of Physics, Boston University, Boston, Massachusetts 02215, USA.

Physical Review. E

|March 15, 2020

Summary

This summary is machine-generated.

Predicting earthquake sizes is challenging, especially near critical points. This study shows that while large, nonscaling events are somewhat predictable, typical earthquakes following the Gutenberg-Richter law are difficult to forecast.

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Last Updated: Dec 26, 2025

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

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Published on: September 23, 2025

Area of Science:

Complex Systems Science
Geophysics
Machine Learning

Background:

Complex systems near criticality are notoriously difficult to predict.
Earthquake size distributions often follow a power-law (Gutenberg-Richter) relationship.
The predictability of earthquakes is a key question in seismology.

Purpose of the Study:

To investigate the predictability of event sizes in a critical system.
To determine if earthquakes, following Gutenberg-Richter scaling, are inherently unpredictable.
To analyze how proximity to criticality affects prediction accuracy.

Main Methods:

Utilized the Olami-Feder-Christensen model to simulate earthquake events.
Employed a convolutional neural network (CNN) for event size prediction.
Examined model behavior at varying degrees of proximity to criticality.

Main Results:

Event sizes in the model follow a power-law with a cutoff for large events.
Predictability of event sizes decreases significantly as the system approaches criticality.
Accurate prediction was only achieved for large, nonscaling events.

Conclusions:

Earthquake faults exhibiting Gutenberg-Richter scaling are inherently difficult to forecast.
The findings suggest limitations in predicting typical earthquake events due to system criticality.
Machine learning can identify predictability in specific event types within complex systems.