Pith. sign in

REVIEW 2 cited by

General framework of the non-perturbative renormalization group for non-equilibrium steady states

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1106.4129 v2 pith:EUX2PUTU submitted 2011-06-21 cond-mat.stat-mech hep-th

classification cond-mat.stat-mechhep-th
keywords nprgframeworkcausalityformalismgeneralgroupnon-equilibriumnon-perturbative
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper is devoted to presenting in detail the non-perturbative renormalization group (NPRG) formalism to investigate out-of-equilibrium systems and critical dynamics in statistical physics. The general NPRG framework for studying non-equilibrium steady states in stochastic models is expounded and fundamental technicalities are stressed, mainly regarding the role of causality and of Ito's discretization. We analyze the consequences of Ito's prescription in the NPRG framework and eventually provide an adequate regularization to encode them automatically. Besides, we show how to build a supersymmetric NPRG formalism with emphasis on time-reversal symmetric problems, whose supersymmetric structure allows for a particularly simple implementation of NPRG in which causality issues are transparent. We illustrate the two approaches on the example of Model A within the derivative expansion approximation at order two, and check that they yield identical results.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Critical dynamics of a scalar field near four spatial dimensions

    hep-th 2026-08 accept novelty 6.0 of 10

    The exactly dissipationless critical dynamics of a scalar field is an invariant but unstable surface of the RG flow, and any small friction drives it to Model A, with new two-loop dynamic exponents.

  2. Solving Functional Renormalization Group Equations with Neural Networks

    hep-ph 2026-03 conditional novelty 6.0 of 10

    A neural network that learns fRG flows from the equation residual, with a large-N analytic baseline, matches finite-difference and discontinuous-Galerkin solvers for O(N) models.

Pith tools