Виявлення пасток у динаміці фрактального броунівського руху з використанням методів кластеризації

Abstract

This study aims to develop a Python-based model of fractional Brownian motion incorporating the effect of traps. The research further investigates this motion using machine learning methods, specifically cluster analysis. The concept of traps is introduced, representing localized regions where a Brownian particle experiences confinement for a defined period. By modeling fractional Brownian motion with incorporated traps, the study utilizes cluster analysis for a comprehensive analysis. The findings not only highlight the significance of incorporating trap mechanisms in Brownian motion modeling but also contribute to the identification and characterization of traps within this framework.