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Register a new userDirected polymers on infinite graphs
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We study the directed polymer model for general graphs (beyond $mathbb Z^d$) and random walks. We provide sufficient conditions for the existence or non-existence of a weak disorder phase, of an $L^2$ region, and of very strong disorder, in terms of properties of the graph and of the random walk. We study in some detail (biased) random walk on various trees including the Galton Watson trees, and provide a range of other examples that illustrate counter-examples to intuitive extensions of the $mathbb Z^d$/SRW result.
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I discuss models for a continuum directed random polymer in a disordered environment in which the polymer lives on a fractal called the textit{diamond hierarchical lattice}, a self-similar metric space forming a network of interweaving pathways. This fractal depends on a branching parameter $bin mathbb{N}$ and a segmenting number $sin mathbb{N}$. For $s>b$ my focus is on random measures on the set of directed paths that can be formulated as a subcritical Gaussian multiplicative chaos. This path measure is analogous to the continuum directed random polymer introduced by Alberts, Khanin, Quastel [Journal of Statistical Physics textbf{154}, 305-326 (2014)].
For a directed graph $G(V_n, E_n)$ on the vertices $V_n = {1,2, dots, n}$, we study the distribution of a Markov chain ${ {bf R}^{(k)}: k geq 0}$ on $mathbb{R}^n$ such that the $i$th component of ${bf R}^{(k)}$, denoted $R_i^{(k)}$, corresponds to the value of the process on vertex $i$ at time $k$. We focus on processes ${ {bf R}^{(k)}: k geq 0}$ where the value of $R_i^{(k+1)}$ depends only on the values ${ R_j^{(k)}: j to i}$ of its inbound neighbors, and possibly on vertex attributes. We then show that, provided $G(V_n, E_n)$ converges in the local weak sense to a marked Galton-Watson process, the dynamics of the process for a uniformly chosen vertex in $V_n$ can be coupled, for any fixed $k$, to a process ${ mathcal{R}_emptyset^{(r)}: 0 leq r leq k}$ constructed on the limiting marked Galton-Watson tree. Moreover, we derive sufficient conditions under which $mathcal{R}^{(k)}_emptyset$ converges, as $k to infty$, to a random variable $mathcal{R}^*$ that can be characterized in terms of the attracting endogenous solution to a branching distributional fixed-point equation. Our framework can also be applied to processes ${ {bf R}^{(k)}: k geq 0}$ whose only source of randomness comes from the realization of the graph $G(V_n, E_n)$.
We prove that the random variable $ct=argmax_{tinrr}{aip(t)-t^2}$ has tails which decay like $e^{-ct^3}$. The distribution of $ct$ is a universal distribution which governs the rescaled endpoint of directed polymers in 1+1 dimensions for large time or temperature.
We prove a distributional limit theorem conjectured in [Journal of Statistical Physics 174, No. 6, 1372-1403 (2019)] for partition functions defining models of directed polymers on diamond hierarchical graphs with disorder variables placed at the graphical edges. The limiting regime involves a joint scaling in which the number of hierarchical layers, $nin mathbb{N}$, of the graphs grows as the inverse temperature, $betaequiv beta(n)$, vanishes with a fine-tuned dependence on $n$. The conjecture pertains to the marginally relevant disorder case of the model wherein the branching parameter $b in {2,3,ldots}$ and the segmenting parameter $s in {2,3,ldots}$ determining the hierarchical graphs are equal, which coincides with the diamond fractal embedding the graphs having Hausdorff dimension two. Unlike the analogous weak-disorder scaling limit for random polymer models on hierarchical graphs in the disorder relevant $b<s$ case (or for the (1+1)-dimensional polymer on the rectangular lattice), the distributional convergence of the partition function when $b=s$ cannot be approached through a term-by-term convergence to a Wiener chaos expansion, which does not exist for the continuum model emerging in the limit. The analysis proceeds by controlling the distributional convergence of the partition functions in terms of the Wasserstein distance through a perturbative generalization of Steins method at a critical step. In addition, we prove that a similar limit theorem holds for the analogous model with disorder variables placed at the vertices of the graphs.
We construct and study a family of continuum random polymer measures $mathbf{M}_{r}$ corresponding to limiting partition function laws recently derived in a weak-coupling regime of polymer models on hierarchical graphs with marginally relevant disorder. The continuum polymers are identified with isometric embeddings of the unit interval $[0,1]$ into a compact diamond fractal with Hausdorff dimension two, and there is a natural probability measure, $mu$, identifiable as being `uniform over the space of continuum polymers, $Gamma$. Realizations of the random measures $mathbf{M}_{r}$ exhibit strong localization properties in comparison to their subcritical counterparts when the diamond fractal has dimension less than two. Whereas two directed paths $p,qin Gamma$ chosen independently according to the pure measure $mu$ have only finitely many intersections with probability one, a realization of the disordered product measure $ mathbf{M}_{r}times mathbf{M}_{r}$ a.s. assigns positive weight to the set of pairs of paths $(p,q)$ whose intersection sets are uncountable but with Hausdorff dimension zero. We give a more refined characterization of the size of these dimension zero sets using generalized (logarithmic) Hausdorff measures. The law of the random measure $mathbf{M}_{r}$ cannot be constructed as a subcritical Gaussian multiplicative chaos because the coupling strength to the Gaussian field would, in a formal sense, have to be infinite.
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