Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2008-04-11
J. Stat. Mech. (2008) P07002
Physics
Condensed Matter
Disordered Systems and Neural Networks
v2=final version
Scientific paper
10.1088/1742-5468/2008/07/P07002
To describe the non-equilibrium dynamics of random systems, we have recently introduced (C. Monthus and T. Garel, arxiv:0802.2502) a 'strong disorder renormalization' (RG) procedure in configuration space that can be defined for any master equation. In the present paper, we analyze in details the properties of the large time dynamics whenever the RG flow is towards some "infinite disorder" fixed point, where the width of the renormalized barriers distribution grows indefinitely upon iteration. In particular, we show how the strong disorder RG rules can be then simplified while keeping their asymptotic exactness, because the preferred exit channel out of a given renormalized valley typically dominates asymptotically over the other exit channels. We explain why the present approach is an explicit construction in favor of the droplet scaling picture where the dynamics is governed by the logarithmic growth of the coherence length $l(t) \sim (\ln t)^{1/\psi}$, and where the statistics of barriers corresponds to a very strong hierarchy of valleys within valleys. As an example of application, we have followed numerically the RG flow for the case of a directed polymer in a two-dimensional random medium. The full RG rules are used to check that the RG flow is towards some infinite disorder fixed point, whereas the simplified RG rules allow to study bigger sizes and to estimate the barrier exponent $\psi$ of the fixed point.
Garel Thomas
Monthus Cecile
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