Pith. sign in

REVIEW 10 cited by

Physics-informed renormalisation group flows

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 2409.13679 v3 pith:QY6ED63C submitted 2024-09-20 hep-th cond-mat.stat-mechhep-ph

classification hep-thcond-mat.stat-mechhep-ph
keywords flowsphysicscomputationaldegreesdynamicalfreedomgroundstate
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

The physics of strongly correlated systems offers some of the most intriguing physics challenges such as competing orders or the emergence of dynamical composite degrees of freedom. Often, the resolution of these physics challenges is computationally hard, but can be simplified enormously by a formulation in terms of the dynamical degrees of freedom and within an expansion about the physical ground state. Importantly, such a formulation does not only reduce or minimise the computational challenges, it also facilitates the access to the physics mechanisms at play. The tasks of finding the dynamical degrees of freedom and the physical ground state can be systematically addressed within the functional renormalisation group approach with flowing fields which accommodates both, emergent composites as well as the physical ground state. In the present work we use this approach to set up physics-informed renormalisation group flows (PIRG flows): Scale-dependent coordinate transformations in field space induce emergent composites, and the respective flows for the effective action generate a large set of target actions, formulated in these emergent composite fields. This novel perspective on RG flows bears a great potential both for conceptual as well as computational applications: to begin with, PIRG flows allow for a systematic search of the dynamical degrees of freedom and the respective ground state that leads to the most rapid convergence of expansion schemes, thus minimising the computational effort. Secondly, the resolution of the remaining computational tasks within a given expansion scheme can be further reduced by optimising the physics content within a given approximation. Thirdly, the maximal variability of PIRG flows can be used to reduce the analytic and numerical effort of solving the flows within a given approximation.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 10 Pith papers

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

  1. Equivalence of effective actions

    hep-th 2025-04 conditional novelty 7.0 of 10

    Genuine field redefinitions and effective (mean field) redefinitions of a quantum field theory generate the same family of effective actions, related by implicit changes of variables that exist to all orders in pertur...

  2. Functional renormalization of QCD in $1 + 1$ dimensions: four-fermion interactions from quark-gluon dynamics

    hep-ph 2024-12 conditional novelty 7.0 of 10

    First functional renormalization group study of two-dimensional QCD with a mass-like regulator, producing flow equations for the gauge coupling, quark mass, and a Fierz-complete set of four-fermion interactions.

  3. The QCD moat regime and its real-time properties

    hep-ph 2024-12 conditional novelty 7.0 of 10

    A first computation of the pion spectral function in the QCD moat regime reveals a quasiparticle peak at nonzero spacelike momentum, the moaton, and indicates no instability toward inhomogeneous chiral condensation fo...

  4. Diffusion Models for Sampling Near Criticality in Lattice Field Theories

    hep-lat 2026-07 accept novelty 6.0 of 10

    Fully convolutional diffusion models trained on small lattices transfer to unseen larger volumes for 2D/3D phi^4 sampling across phases, matching or beating same-size training on most observables.

  5. 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.

  6. Self-consistent graviton spectral function in Lorentzian quantum gravity

    hep-th 2025-07 conditional novelty 6.0 of 10

    A self-consistent spectral renormalisation group computation yields a positive, normalizable graviton spectral function with a massless pole and a multi-graviton continuum decaying as 1/(λ² log³ λ²).

  7. Functional Renormalization Group meets Computational Fluid Dynamics: RG flows in a multi-dimensional field space

    cond-mat.stat-mech 2024-12 conditional novelty 6.0 of 10

    A two-dimensional Kurganov-Tadmor finite-volume scheme accurately solves FRG flow equations for effective potentials in multi-dimensional field space, benchmarked against exact zero-dimensional path integrals and appl...

  8. DiFfRG: A Discretisation Framework for functional Renormalisation Group flows

    hep-ph 2024-12 conditional novelty 6.0 of 10

    DiFfRG provides a general, code-generated numerical toolkit for fRG flows, demonstrated on O(N), Quark-Meson, Yang-Mills, and four-fermion systems.

  9. Super-Resolving Normalising Flows for Lattice Field Theories

    hep-lat 2024-12 conditional novelty 6.0 of 10

    An RG-inspired normalising flow with exact likelihood tracking samples 2D phi^4 theory on 128x128 lattices using only 4x4 MCMC samples, keeping high ESS/N in symmetric and broken phases.

  10. Critical scaling for spectral functions

    hep-th 2025-06 conditional novelty 5.0 of 10

    A spectral renormalisation group computation extracts the anomalous dimension eta ~ 0.1 for 2+1-dimensional phi^4 theory in the scaling regime, within a truncated approximation.

Pith tools