AI-Driven Control of Chaos: A Transformer-Based Approach for Dynamical Systems
Chaotic Dynamics
2025-06-18 v2
Abstract
Chaotic behavior in dynamical systems poses a significant challenge in trajectory control, traditionally relying on computationally intensive physical models. We present a machine learning-based algorithm to compute the minimum control bounds required to confine particles within a region indefinitely, using only samples of orbits that iterate within the region before diverging. This model-free approach achieves high accuracy, with a mean squared error of and computation times in the range of seconds. The results highlight its efficiency and potential for real-time control of chaotic systems.
Cite
@article{arxiv.2412.17357,
title = {AI-Driven Control of Chaos: A Transformer-Based Approach for Dynamical Systems},
author = {David Valle and Rubén Capeáns and Alexandre Wagemakers and Miguel A. F. Sanjuán},
journal= {arXiv preprint arXiv:2412.17357},
year = {2025}
}