Stochastic processes under driving: self-organised criticality, active matter and the climate system
File(s)
Author(s)
Chen, Letian
Type
Thesis
Abstract
Stochastic processes are ubiquitous in nature, ranging from atomic-scale movements to the evolution of natural systems. When external driving is introduced, these systems experience a departure from thermodynamic equilibrium, exhibiting fascinating phenomena and surprising statistical properties, including long-range spatiotemporal correlations and critical behaviour.
In this thesis, I have focused on the statistical properties of three typical stochastic processes under driving: self-organised criticality (Chapter 2), active matter (Chapters 3, 4 and 5), and the climate system (Chapter 6). In self-organised criticality, systems naturally evolve to a critical state under external driving, with the spreading of long-range spatial correlations. In active matter systems, particles consume external energy to move or exert mechanical forces, exhibiting phenomena that cannot exist in passive systems. In the climate system, climate change can be considered as a stochastic process, with human activities serving as external driving forces. Detecting and attributing the effects of these external driving forces is crucial for understanding and mitigating climate change impacts.
In this thesis, I have focused on the statistical properties of three typical stochastic processes under driving: self-organised criticality (Chapter 2), active matter (Chapters 3, 4 and 5), and the climate system (Chapter 6). In self-organised criticality, systems naturally evolve to a critical state under external driving, with the spreading of long-range spatial correlations. In active matter systems, particles consume external energy to move or exert mechanical forces, exhibiting phenomena that cannot exist in passive systems. In the climate system, climate change can be considered as a stochastic process, with human activities serving as external driving forces. Detecting and attributing the effects of these external driving forces is crucial for understanding and mitigating climate change impacts.
Version
Open Access
Date Issued
2024-11-05
Date Awarded
01/04/2025
License URL
Advisor
Pruessner, Gunnar
Publisher Department
Department of Mathematics
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)