Title:

Nonequilibrium steady states from a randomwalk perspective

It is well known that at thermal equilibrium (whereby a system has settled into a steady state with no energy or mass being exchanged with the environment), the microstates of a system are exponentially weighted by their energies, giving a Boltzmann distribution. All macroscopic quantities, such as the free energy and entropy, can be in principle computed given knowledge of the partition function. In a nonequilibrium steady state, on the other hand, the system has settled into a stationary state, but some currents of heat or mass persist. In the presence of these currents, there is no unified approach to solve for the microstate distribution. This motivates the central theme of this work, where I frame and solve problems in nonequilibrium statistical physics in terms of random walk and diffusion problems. The system that is the focus of Chapters 2, 3, and 4 is the (Totally) Asymmetric Simple Exclusion Process, or (T)ASEP. This is a system of hardcore particles making jumps through an open, onedimensional lattice. This is a paradigmatic example of a nonequilibrium steady state that exhibits phase transitions. Furthermore, the probability of an arbitrary configuration of particles is exactly calculable, by a matrix product formalism that lends a natural association between the ASEP and a family of random walk problems. In Chapter 2 I present a unified description of the various combinatorial interpretations and mappings of steadystate configurations of the ASEP. As well as deriving new results, I bring together and unify results and observations that have otherwise been scattered in the combinatorics and physics literature. I show that particular particle configurations of the ASEP have a onetomany mapping to a set of more abstract paths, which themselves have a onetomany mapping to permutations of numbers. One observation from this wider literature has been that this mapped space can be interpreted as a larger set of configurations in some equilibrium system. This naturally gives an interpretation of ASEP configuration probabilities as summations of Boltzmann weights. The nonequilibrium partition function of the ASEP is then a summation over this equilibrium ensemble, however one encounters difficulties when calculating more detailed measures of this state space, such as the entropy. This motivates the work in Chapter 3. I calculate a quantity known as the Rényi entropy, which is a measure of the partitioning of the state space, and a deformation of the familiar Shannon entropy. The Rényi entropy is simple for an equilibrium system, but has yet to be explored in a classical nonequilibrium steady state. I use insights from Chapter 2 to frame one of these Rényi entropies  requiring the enumeration of the squares of configuration weights  in terms of a twodimensional random walk with absorbing boundaries. I find the appropriate generating function across the full phase diagram of the TASEP by generalising a mathematical technique known as the obstinate kernel method. Importantly, this nonequilibrium Rényi entropy has a different structural form to any equilibrium system, highlighting a clear distinction between equilibrium and nonequilibrium distributions. In Chapter 4 I continue to examine the Rényi entropy of the TASEP, but now performing a time and space continuum limit of the random walk problem in Chapter 3. The resultant problem is a twodimensional dffusion problem with absorbing boundary conditions, which once solved should recover TASEP dynamics about the point in the phase diagram where the three dynamical phases meet. I derive a generating function, sufficiently simple that its singularities can be analysed by hand. This calculation entails a novel generalisation of the obstinate kernel method of Chapter 3: I find a solution by exploiting a symmetry in the Laplace transform of the diffusion equation. I finish in Chapter 5 by introducing and solving another nonequilibrium system, termed the manyfilament Brownian ratchet. This comprises an arbitrary number of filaments that stochastically grow and contract, with the net effect of moving a driftdiffusing membrane by purely from thermal fluctuations and steric interactions. These dynamics draw parallels with those of actin filament networks at the leading edge of eukaryotic cells, and this improves on previous 'pure ratchet' models by introducing interactions and heterogeneity in the filaments. I find an Ndimensional diffusion equation for the evolution of the N filamentmembrane displacements. Several parameters can be varied in this system: the drift and diffusion rates of each of the filaments and membrane, the strength of a quadratic interaction between each filament with the membrane, and the strength of a surface tension across the filaments. For several interesting physical cases I find the steadystate distribution exactly, and calculate how the mean velocity of the membrane varies as a function of these parameters.
