Дослідження динаміки броуновських частинок у системах із пастками за допомогою машинного навчання

Abstract

The aim of this study is to model Brownian motion with traps using Python and to analyze the motion using clustering analysis with machine learning techniques. In this study, we have introduced the concept of traps, which are the regions in space where the Brownian particle is confined for a certain period of time. We have simulated Brownian motion with traps and analyzed the motion using clustering analysis. The results of this study are important because they demonstrate the significance of considering trapping mechanisms when modeling Brownian motion with traps. Moreover, this study is crucial for the detection and characterization of traps in Brownian motion, which can have implications for various fields such as physics, chemistry, and biophysics.