Large Deviations of Irreversible Processes
Mikola C. Schlottke

TL;DR
This thesis analyzes the fluctuations of time-irreversible stochastic processes using large deviation theory, with applications to molecular motors, MCMC algorithms, and singular Fokker-Planck equations, revealing insights into their asymptotic behavior.
Contribution
It introduces novel techniques connecting large deviations with Hamilton-Jacobi equations and applies them to diverse irreversible systems, including molecular motors and stochastic PDEs.
Findings
Hamilton-Jacobi equations characterize process fluctuations.
Maximal irreversibility optimizes MCMC convergence.
New variational approach for singular Fokker-Planck limits.
Abstract
Time-irreversible stochastic processes are frequently used in natural sciences to explain non-equilibrium phenomena and to design efficient stochastic algorithms. Our main goal in this thesis is to analyse their dynamics by means of large deviation theory. We focus on processes that become deterministic in a certain limit, and characterize their fluctuations around that deterministic limit by Lagrangian rate functions. Our main techniques for establishing these characterizations rely on the connection between large deviations and Hamilton-Jacobi equations. We sketch this connection with examples in the introductory parts of this thesis. The second part of the thesis is devoted to irreversible processes that are motivated from molecular motors, Markov chain Monte Carlo (MCMC) methods and stochastic slow-fast systems. We characterize the asymptotic dynamics of molecular motors by…
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Taxonomy
TopicsAdvanced Thermodynamics and Statistical Mechanics · Markov Chains and Monte Carlo Methods · Mathematical Biology Tumor Growth
