REVIEW 8 cited by
Sampling the lattice Nambu-Goto string using Continuous Normalizing 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
Signed reviews
read the original abstract
Effective String Theory (EST) represents a powerful non-perturbative approach to describe confinement in Yang-Mills theory that models the confining flux tube as a thin vibrating string. EST calculations are usually performed using the zeta-function regularization: however there are situations (for instance the study of the shape of the flux tube or of the higher order corrections beyond the Nambu-Goto EST) which involve observables that are too complex to be addressed in this way. In this paper we propose a numerical approach based on recent advances in machine learning methods to circumvent this problem. Using as a laboratory the Nambu-Goto string, we show that by using a new class of deep generative models called Continuous Normalizing Flows it is possible to obtain reliable numerical estimates of EST predictions.
Forward citations
Cited by 8 Pith papers
-
Stochastic Quantization as Optimal Control
Stochastic quantization is re-expressed as finite-time optimal control, in which a learned Doob force plus exact path weights reach the Gibbs measure without waiting for equilibrium.
-
Exploring Generative Networks for Manifolds with Non-Trivial Topology
A GFlowNet-inspired diffusion sampler is proposed and shown, on toy and 2D lattice scalar models, to generate configurations across disconnected sectors that normalizing flows and plain diffusion models miss.
-
Scaling of Stochastic Normalizing Flows in $\mathrm{SU}(3)$ lattice gauge theory
First demonstration that Stochastic Normalizing Flows inherit the linear-with-volume scaling of non-equilibrium MCMC in 4D SU(3) lattice gauge theory, with a factor-of-two efficiency gain.
-
Studying Effective String Theory using deep generative models
Flow-based samplers numerically confirm the next-to-leading-order width and the resummed string-tension conjecture for the Nambu-Goto effective string in 2+1 dimensions.
-
Symmetry-preserving neural networks in lattice field theories
Translation- and gauge-equivariant neural networks (L-CNNs) predict Wilson loops, topological charge, and flux observables with orders-of-magnitude lower error than symmetry-breaking baselines, and neural gradient flo...
-
Diffusion models and stochastic quantisation in lattice field theory
Diffusion models, whose backward denoising step resembles stochastic quantisation, can learn from HMC data to generate configurations for 2D scalar lattice field theory.
-
Machine-learning approaches to accelerating lattice simulations
A review of unbiased machine-learning acceleration methods for lattice field theory, covering flow-based sampling, contour deformations, control variates, and surrogate observables.
- Simulating the Hubbard Model with Equivariant Normalizing Flows
Discussion (0). Continue with ORCID to comment.