Deciphering Complexity: Machine Learning Insights into Chaotic Dynamical Systems

Fuente: arXiv
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Autor principal: Osmanov, Lazare
Formato: Preprint
Publicado: 2024
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author Osmanov, Lazare
author_facet Osmanov, Lazare
contents We introduce new machine-learning techniques for analyzing chaotic dynamical systems. The primary objectives of the study include the development of a new and simple method for calculating the Lyapunov exponent using only two trajectory data points unlike traditional methods that require an averaging procedure, the exploration of phase transition graphs from regular periodic to chaotic dynamics to identify "almost integrable" trajectories where conserved quantities deviate from whole numbers, and the identification of "integrable regions" within chaotic trajectories. These methods are applied and tested on two dynamical systems: "Two objects moving on a rod" and the "Henon-Heiles" systems.
format Preprint
id arxiv_https___arxiv_org_abs_2408_02005
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Deciphering Complexity: Machine Learning Insights into Chaotic Dynamical Systems
Osmanov, Lazare
Chaotic Dynamics
Classical Physics
We introduce new machine-learning techniques for analyzing chaotic dynamical systems. The primary objectives of the study include the development of a new and simple method for calculating the Lyapunov exponent using only two trajectory data points unlike traditional methods that require an averaging procedure, the exploration of phase transition graphs from regular periodic to chaotic dynamics to identify "almost integrable" trajectories where conserved quantities deviate from whole numbers, and the identification of "integrable regions" within chaotic trajectories. These methods are applied and tested on two dynamical systems: "Two objects moving on a rod" and the "Henon-Heiles" systems.
title Deciphering Complexity: Machine Learning Insights into Chaotic Dynamical Systems
topic Chaotic Dynamics
Classical Physics
url https://arxiv.org/abs/2408.02005