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Physics-informed renormalisation group flows

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

Physics-informed renormalisation group flows

classification hep-th cond-mat.stat-mechhep-ph
keywords flowsphysicscomputationaldegreesdynamicalfreedomgroundstate
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

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

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

    hep-lat 2026-07 accept novelty 6.0

    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.

  2. Physics-informed operator flows and observables

    hep-th 2025-07 unverdicted novelty 6.0

    Operator PIRGs complete the prior PIRG method by enabling computation of all correlation functions, demonstrated analytically in zero-dimensional phi^4 theory via vertex expansion to ten-point functions.