Title: Topological Phase Transitions in Non-Equilibrium Systems

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper investigates the emergence of topological phase transitions in non-equilibrium systems, proposing a novel method for quantifying and predicting these transitions through the dynamic analysis of network topology. Traditional approaches often rely on static measurements, lacking the ability to capture the intricate, evolving nature of these phenomena. We introduce a computational framework based on graph neural networks to model and analyze the fractal properties of network topology, providing a fundamentally different perspective on identifying and predicting topological phase transitions. The core mechanism centers on continuous, dynamic network representations, offering enhanced predictive capabilities compared to existing methods. This work presents a rigorous investigation of the impact of temperature and disorder on network topology, demonstrating a significant improvement in prediction accuracy.

DOI: 10.5281/zenodo.22243608

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