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We study random two-dimensional spanning forests in the plane that can be viewed both in the discrete case and in their appropriately taken scaling limits as a uniformly chosen spanning tree with some Poissonian deletion of edges or points.…

Probability · Mathematics 2020-08-04 Stéphane Benoist , Laure Dumaz , Wendelin Werner

We investigate the entanglement-renormalization group flows of translation-invariant topological stabilizer models in three dimensions. Fracton models are observed to bifurcate under entanglement renormalization, generically returning at…

Strongly Correlated Electrons · Physics 2020-07-15 Arpit Dua , Pratyush Sarkar , Dominic J. Williamson , Meng Cheng

We develop a flow renormalization approach for periodically-driven quantum systems, which reveals prethermal dynamical regimes and associated timescales via direct correspondence between real time and flow time behavior. In this formalism,…

Quantum Physics · Physics 2021-03-16 Martin Claassen

We investigate non-equilibrium critical phenomena using a nonperturbative renormalization group method. Reaction-diffusion processes are described by a scale dependent effective action which evolution is governed by very generic flow…

Statistical Mechanics · Physics 2011-07-19 Léonie Canet , Bertrand Delamotte , Olivier Deloubrière , Nicolas Wschebor

A renormalization group flow equation with a scale-dependent transformation of field variables gives a unified description of fundamental and composite degrees of freedom. In the context of the effective average action, we study the…

High Energy Physics - Theory · Physics 2009-11-07 Holger Gies , Christof Wetterich

Recently, the connections between gradient flow and renormalization group have been explored analytically and numerically. Gradient flow (when modified by a field rescaling) can be characterized as a continuous blocking transformation. In…

High Energy Physics - Lattice · Physics 2019-12-05 Andrea Carosso , Anna Hasenfratz , Ethan T. Neil

An analysis is made of various methods of phenomenological renormalization based on finite-size scaling equations for inverse correlation lengths, the singular part of the free energy density, and their derivatives. The analysis is made…

Statistical Mechanics · Physics 2009-11-07 M. A. Yurishchev

The renormalization group flow of an integrable two dimensional quantum field theory which contains unstable particles is investigated. The analysis is carried out for the Virasoro central charge and the conformal dimensions as a function…

High Energy Physics - Theory · Physics 2009-12-31 O. A. Castro-Alvaredo , A. Fring

We review the gradient flow for gauge and fermion fields and its applications to lattice gauge theory computations. Using specific examples, we discuss the interplay between perturbative and non-perturbative calculations in the context of…

High Energy Physics - Lattice · Physics 2023-01-19 Andrea Shindler

The perturbative renormalization of the Ginzburg-Landau model is reconsidered based on the Feynman diagram technique. We derive renormalization group (RG) flow equations, exactly calculating all vertices appearing in the perturbative…

Statistical Mechanics · Physics 2011-08-29 J. Kaupuzs

We prove that, in the FK-percolation model, the probabilities of local events are uniformly analytic in the percolation parameter $p$ under suitable mixing assumptions on the measure, and satisfy a uniform exponential growth bound. This…

Probability · Mathematics 2026-04-21 Lucas D'Alimonte , Loïc Gassmann

Irreversible aggregation is revisited in view of recent work on renormalization of complex networks. Its scaling laws and phase transitions are related to percolation transitions seen in the latter. We illustrate our points by giving the…

Data Analysis, Statistics and Probability · Physics 2011-08-26 Seung-Woo Son , Golnoosh Bizhani , Claire Christensen , Peter Grassberger , Maya Paczuski

The pomeron flux renormalization hypothesis is reviewed and presented as a scaling law in diffraction. Predictions for soft and hard diffraction based on pomeron flux scaling are compared with experimental results.

High Energy Physics - Phenomenology · Physics 2007-05-23 Konstantin Goulianos

We introduce graph normalizing flows: a new, reversible graph neural network model for prediction and generation. On supervised tasks, graph normalizing flows perform similarly to message passing neural networks, but at a significantly…

Machine Learning · Computer Science 2019-05-31 Jenny Liu , Aviral Kumar , Jimmy Ba , Jamie Kiros , Kevin Swersky

Normalizing flows model complex probability distributions by combining a base distribution with a series of bijective neural networks. State-of-the-art architectures rely on coupling and autoregressive transformations to lift up invertible…

Machine Learning · Computer Science 2021-02-15 Antoine Wehenkel , Gilles Louppe

We study graphical representations for two related models. The first model is the transverse field quantum Ising model, an extension of the original Ising model which was introduced by Lieb, Schultz and Mattis in the 1960's. The second…

Probability · Mathematics 2010-11-12 Jakob E. Björnberg

Normalizing flows are a class of generative models that enable exact likelihood evaluation. While these models have already found various applications in particle physics, normalizing flows are not flexible enough to model many of the…

High Energy Physics - Phenomenology · Physics 2022-09-07 Rob Verheyen

In this work, we employ a field-theoretic renormalization group approach to study a paradigmatic model of directed percolation. We focus on the perturbative calculation of the equation of state, extending the analysis to the three-loop…

Statistical Mechanics · Physics 2026-02-13 Michal Hnatič , Matej Kecer , Tomáš Lučivjanský , Lukáš Mižišin

The renormalization group transformation for extreme value statistics of independent, identically distributed variables, recently introduced to describe finite size effects, is presented here in terms of a partial differential equation…

Statistical Mechanics · Physics 2011-01-06 Eric Bertin , Géza Györgyi

We introduce a method for reconstructing an infinitesimal normalizing flow given only an infinitesimal change to a (possibly unnormalized) probability distribution. This reverses the conventional task of normalizing flows -- rather than…

Machine Learning · Statistics 2020-12-04 David Pfau , Danilo Rezende