Non-Linear Problems in Interacting Particle Systems
Gabriel Nahum
Abstract
We present three works on Interacting Particle Systems where some form of non-linearity arises. In the context of the description of Non-Equilibrium Steady States (NESS), we formulate a Matrix Product Ansatz in order to describe the NESS of a process defined on the lattice $\Lambda_N = \{1, \dots, N\}$, evolving as the SSEP in $\{2, \dots, N - 1\}$ and coupled with two-site reaction-diffusion processes at the boundaries, acting on the pairs of sites $\{1,2\}$ and $\{N-1,N\}$. These pairs of sites can take four different states each, leading to 12 possible transitions between them. We derive a set of constraints where the underlying quadratic algebra is consistent, which are related with the correlation of the reservoirs, present the representation of the objects in the formulation, and provide examples of rates satisfying these constraints. In the second work, our focus is in generalizing the Porous Media Model (PMM), associated with the hydrodynamic equation $\partial_t \rho = \partial^2 u \rho^m$ for $m \in \mathbb{N}^+$, into a universal exclusion family parametrized by $m \in \mathbb{R}$, and, in particular, representing the transition from the slow diffusion regime ($m > 1$) to the fast diffusion regime ($m < 1$). We successfully treat the case $m \in (0,2)$, encompassing the phase transition $m = 1$, in this manner deriving the Porous Media Equation in the range $m \in (1,2)$, and the Fast Diffusion Equation for $m > 0$. Next, we generalize the PMM in another direction: constructing the diffusion coefficient $D(\rho) = \rho^n (1 - \rho)^k$ with $n,k \in \mathbb{N}^+$. The generalized PMM that we introduce is a gradient model that inherits theoretical properties from PMM, as, for instance, the presence of mobile clusters and blocked configurations. The construction of the model is indirect and delicate, in order to maintain the gradient property. We then generalize this into a long-range dynamics inheriting the gradient property. The long-range generalization of the dynamics is simple and can be applied to any exclusion process.