Data-Driven Identification of Attractors Using Machine Learning
Abstract
Abstract. In this paper we explore challenges in developing a topological framework in which machine learning can be used to robustly characterize global dynamics. Specifically, we focus on learning a useful discretization of the phase space of a flow on a compact, hyperrectangle in [Formula: see text] from a neural network trained on labeled orbit data. A characterization of the structure of the global dynamics is obtained from approximations of attracting neighborhoods provided by the phase space discretization. The perspective that motivates this work is based on Conley’s topological approach to dynamics, which provides a means to evaluate the efficacy and efficiency of our approach.
Fields of Study & Subjects
Topological and Geometric Data Analysis
Quantum chaos and dynamical systems
Model Reduction and Neural Networks
Publication details
- Journal / source
- SIAM Journal on Applied Dynamical Systems
- Volume
- 25
- Issue
- 2
- Pages
- 836–860
- Publication date
- 13 April 2026
- Language
- EN