Directed abelian algebras and their applications to stochastic models
Francisco C. Alcaraz, Vladimir Rittenberg

TL;DR
This paper introduces directed abelian algebras (DAA) linked to directed acyclic graphs, enabling algebraic analysis of stochastic models like sandpiles, revealing critical spectra and avalanche behaviors in various dimensions.
Contribution
It develops the theory of DAA, applies it to stochastic models including sandpiles, and analyzes their spectral properties and avalanche dynamics in different dimensions.
Findings
Spectrum is gapless with dynamic exponent z=D in D-dimensional lattices.
In 1D, avalanches follow the random walker universality class with exponent 3/2.
In 2D, avalanche size distribution exponent is approximately 1.782.
Abstract
To each directed acyclic graph (this includes some D-dimensional lattices) one can associate some abelian algebras that we call directed abelian algebras (DAA). On each site of the graph one attaches a generator of the algebra. These algebras depend on several parameters and are semisimple. Using any DAA one can define a family of Hamiltonians which give the continuous time evolution of a stochastic process. The calculation of the spectra and ground state wavefunctions (stationary states probability distributions) is an easy algebraic exercise. If one considers D-dimensional lattices and choose Hamiltonians linear in the generators, in the finite-size scaling the Hamiltonian spectrum is gapless with a critical dynamic exponent . One possible application of the DAA is to sandpile models. In the paper we present this application considering one and two dimensional lattices. In the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStochastic processes and statistical mechanics · Theoretical and Computational Physics · Complex Network Analysis Techniques
