REVIEW 9 cited by
Sampling using $SU(N)$ gauge equivariant 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
abstract
We develop a flow-based sampling algorithm for $SU(N)$ lattice gauge theories that is gauge-invariant by construction. Our key contribution is constructing a class of flows on an $SU(N)$ variable (or on a $U(N)$ variable by a simple alternative) that respect matrix conjugation symmetry. We apply this technique to sample distributions of single $SU(N)$ variables and to construct flow-based samplers for $SU(2)$ and $SU(3)$ lattice gauge theory in two dimensions.
Forward citations
Cited by 9 Pith papers
-
Higher-order hopping-parameter expansion by human-AI collaboration
Trie-structured algorithms compute κ^8 to κ^12 terms in the hopping expansion of Tr ln M at costs scaling from 20x to 8900x a staple, verified by direct comparison to a reference calculation.
-
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.
-
Progress in Normalizing Flows for 4d Gauge Theories
Learned active loops improve spectral flow models, and correlated flow ensembles reduce statistical errors by 2-3x in Nf=2 QCD for the pion gluon momentum fraction, with a computational advantage after roughly 4,000 c...
-
Normalizing flows for SU($N$) gauge theories employing singular value decomposition
A new SVD-based construction makes normalizing flow transformations for SU(N) gauge links automatically gauge-equivariant, with promising training efficiency on a 4^4 SU(3) lattice.
-
Pre-Strings Lectures on Artificial Intelligence
Lecture notes define neural-network field theory and survey how it recovers known QFT/string results plus applied AI techniques for string problems.
-
Diffusion Models for SU(2) Lattice Gauge Theory in Two Dimensions
A flat-space quaternion diffusion model, trained at β=2.0 on an 8×8 lattice, reproduces the exact SU(2) plaquette to |Δ|≤0.001 near the training coupling and within 0.06 over β∈[1,4].
-
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...
-
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.