AI-Driven Control of Chaos: A Transformer-Based Approach for Dynamical Systems

2024/12/23 by David Valle, Rubén Capeáns, Valle, David +5 · 1 citation
Computer Science · #Chaotic Dynamics (nlin.CD) #FOS: Physical sciences #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2412.17357

openalex publication_date 2024/12/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 2.88 × 10^-4 and computation times in the range of seconds. The results highlight its efficiency and potential for real-time control of chaotic systems.

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• On the Generalisation of Koopman Representations for Chaotic System Control

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