Paper Citation Record · LEDGER

Machine-learning approaches to accelerating lattice simulations

As of 9 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 2 inbound Pith citation observations for arXiv:2502.02670.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.02670 v2

Coverage vector

measured 87 of 87 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:38:42.403610Z

measured 89 of 89 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-11T01:54:10.924145Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T04:10:58.613619Z

Reference resolution

87 of 87 outbound references displayed

verified exact16
verified fuzzy3
unresolved66
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cfbdc1b2-9e25-45de-a97e-12569de7fa79 · outbound

This paper cites Machine learning spectral functions in lattice QCD.

Machine-learning approaches to accelerating lattice simulations Machine learning spectral functions in lattice QCD

Reference 1

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Observation 515f152c-36ca-4d62-b45b-ab6854c2f84d · outbound

This paper cites Offler,A study of thermal NRQCD with machine learning methods, Ph.D.

Machine-learning approaches to accelerating lattice simulations Offler,A study of thermal NRQCD with machine learning methods, Ph.D

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.135210Z

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Observation 4120c2e6-705e-4b72-814e-18063b5ed4a7 · outbound

This paper cites Fournier, L.

Machine-learning approaches to accelerating lattice simulations Fournier, L

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.140387Z

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Observation 681d738e-ee52-4135-bd76-fd379b3c8f78 · outbound

This paper cites Kades, J.M.

Machine-learning approaches to accelerating lattice simulations Kades, J.M

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.145226Z

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Observation 3e0076f6-a75b-4383-b7e7-f870e2b6feea · outbound

This paper cites Reconstructing spectral functions via automatic differentiation.

Machine-learning approaches to accelerating lattice simulations Reconstructing spectral functions via automatic differentiation

Reference 5

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Observation 6ab68ccc-59d7-4c55-83ab-247e1e9a713f · outbound

This paper cites Carrasquilla and R.G.

Machine-learning approaches to accelerating lattice simulations Carrasquilla and R.G

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.156019Z

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Observation e09981aa-cabd-46d2-89cb-d3a164265fb7 · outbound

This paper cites Machine learning phases of an Abelian gauge theory.

Machine-learning approaches to accelerating lattice simulations Machine learning phases of an Abelian gauge theory

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:44.792894Z

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Observation 12ceb929-5f9a-4014-a9e8-de146c1a1a5d · outbound

This paper cites Detection of phase transition via convolutional neural network.

Machine-learning approaches to accelerating lattice simulations Detection of phase transition via convolutional neural network

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:44.771580Z

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Observation 6aa5e3d0-bd94-421f-b694-2c76f3e24cef · outbound

This paper cites Learning phase transitions by confusion.

Machine-learning approaches to accelerating lattice simulations Learning phase transitions by confusion

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-08-09T11:38:44.715166Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3964cf09-1144-495e-a352-7313c75c9f2e · outbound

This paper cites Identifying topological order through unsupervised machine learning.

Machine-learning approaches to accelerating lattice simulations Identifying topological order through unsupervised machine learning

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-08-09T11:38:44.561841Z

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Observation 1e338804-0734-4ce4-9a7c-967c9b22e059 · outbound

This paper cites Broecker, J.

Machine-learning approaches to accelerating lattice simulations Broecker, J

Reference 11

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Observation a8156289-0d48-49e6-92af-1c6429a05bb8 · outbound

This paper cites Machine learning action parameters in lattice quantum chromodynamics.

Machine-learning approaches to accelerating lattice simulations Machine learning action parameters in lattice quantum chromodynamics

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.186519Z

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Observation 3d8d0416-e4e2-455c-9262-42e69492a5f1 · outbound

This paper cites Carleo and M.

Machine-learning approaches to accelerating lattice simulations Carleo and M

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.244656Z

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Observation 2a5b584e-55df-4e74-a43c-783ab56ca45c · outbound

This paper cites an unresolved cited work.

Machine-learning approaches to accelerating lattice simulations Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.312913Z

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Observation 60714c6c-57d3-4798-904f-8a08cf0a9960 · outbound

This paper cites Gauge equivariant neural networks for quantum lattice gauge theories.

Machine-learning approaches to accelerating lattice simulations Gauge equivariant neural networks for quantum lattice gauge theories

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.317655Z

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Observation 01090884-7f5f-4bc1-86b5-537e30988203 · outbound

This paper cites Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural network.

Machine-learning approaches to accelerating lattice simulations Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural network

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.321971Z

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Observation 0ec3f75b-4dff-4f5c-9c80-be49db90febf · outbound

This paper cites Flow-based sampling for lattice field theories.

Machine-learning approaches to accelerating lattice simulations Flow-based sampling for lattice field theories

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.327807Z

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Observation 855b3b58-3367-4ed7-8e6d-28ef0da69dcc · outbound

This paper cites Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines.

Machine-learning approaches to accelerating lattice simulations Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.333056Z

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Observation fdd5e7e6-d91c-4037-8b7f-79d2fc31086d · outbound

This paper cites Towards reduction of autocorrelation in HMC by machine learning.

Machine-learning approaches to accelerating lattice simulations Towards reduction of autocorrelation in HMC by machine learning

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:44.450216Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4f6a52b1-c5cf-4d31-baf7-ab2cac4ee8cd · outbound

This paper cites Diffusion Models as Stochastic Quantization in Lattice Field Theory.

Machine-learning approaches to accelerating lattice simulations Diffusion Models as Stochastic Quantization in Lattice Field Theory

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.343323Z

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Observation 1864804f-8d3e-4311-a140-1f63244e8651 · outbound

This paper cites Generative Diffusion Models for Lattice Field Theory.

Machine-learning approaches to accelerating lattice simulations Generative Diffusion Models for Lattice Field Theory

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.348734Z

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Observation 538f2bdb-c26e-44a8-9338-a90bd029e8fd · outbound

This paper cites Regressive and generative neural networks for scalar field theory.

Machine-learning approaches to accelerating lattice simulations Regressive and generative neural networks for scalar field theory

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.354041Z

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Observation 5fa9c2fe-9417-439a-9c19-551cd85697db · outbound

This paper cites Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks.

Machine-learning approaches to accelerating lattice simulations Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.359350Z

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Observation 1b866258-7cc9-4c2b-aa76-8707a27f60dc · outbound

This paper cites Box and M.E.

Machine-learning approaches to accelerating lattice simulations Box and M.E

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:38:44.873198Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e2effed2-8fd2-43de-bd84-cd25d4b6c990 · outbound

This paper cites Flow-based generative models for Markov chain Monte Carlo in lattice field theory.

Machine-learning approaches to accelerating lattice simulations Flow-based generative models for Markov chain Monte Carlo in lattice field theory

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.369798Z

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Observation 33a0e878-c0fa-4d96-be87-3eb145ea59c4 · outbound

This paper cites Nicolai,On a New Characterization of Scalar Supersymmetric Theories,Phys.

Machine-learning approaches to accelerating lattice simulations Nicolai,On a New Characterization of Scalar Supersymmetric Theories,Phys

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:38:44.857491Z

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Observation 5ab8ae77-14c2-40ea-977b-5ae59669e0ef · outbound

This paper cites Trivializing maps, the Wilson flow and the HMC algorithm.

Machine-learning approaches to accelerating lattice simulations Trivializing maps, the Wilson flow and the HMC algorithm

Reference 27

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Observation 3c2d3641-8c12-4cc6-b4ee-7d39cede0dfe · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

Machine-learning approaches to accelerating lattice simulations NICE: Non-linear Independent Components Estimation

Reference 28

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Observation 0029b514-c472-4fe8-92b3-548dcca5437f · outbound

This paper cites Density estimation using Real NVP.

Machine-learning approaches to accelerating lattice simulations Density estimation using Real NVP

Reference 29

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Observation f890c33e-fdf4-4f32-a460-e2490ef137de · outbound

This paper cites Glow: Generative Flow with Invertible 1x1 Convolutions.

Machine-learning approaches to accelerating lattice simulations Glow: Generative Flow with Invertible 1x1 Convolutions

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.570932Z

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Observation 56660947-c66b-49a4-a119-ed52e523e2d0 · outbound

This paper cites Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows.

Machine-learning approaches to accelerating lattice simulations Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows

Reference 31

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Observation 77146e41-4190-43b6-a46f-9c463ffe8143 · outbound

This paper cites Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows.

Machine-learning approaches to accelerating lattice simulations Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

Reference 32

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Observation 52b565aa-d26c-42e5-b920-2789ce14232e · outbound

This paper cites Asymptotically unbiased estimation of physical observables with neural samplers.

Machine-learning approaches to accelerating lattice simulations Asymptotically unbiased estimation of physical observables with neural samplers

Reference 33

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Observation 1878aecf-35b4-4be6-9440-696fede8c523 · outbound

This paper cites Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models.

Machine-learning approaches to accelerating lattice simulations Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.593704Z

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Observation baa0bfec-df45-40b0-9d25-534cdd8b62cd · outbound

This paper cites Sampling using $SU(N)$ gauge equivariant flows.

Machine-learning approaches to accelerating lattice simulations Sampling using $SU(N)$ gauge equivariant flows

Reference 35

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Observation ac6824fd-938b-4965-b259-1fbd276b31ad · outbound

This paper cites Equivariant flow-based sampling for lattice gauge theory.

Machine-learning approaches to accelerating lattice simulations Equivariant flow-based sampling for lattice gauge theory

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.603710Z

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Observation 2d7f1dbf-2313-43c6-b460-c9ad592e548b · outbound

This paper cites Kanwar,Machine Learning and Variational Algorithms for Lattice Field Theory, Ph.D.

Machine-learning approaches to accelerating lattice simulations Kanwar,Machine Learning and Variational Algorithms for Lattice Field Theory, Ph.D

Reference 37

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Observation 9e5daaa2-f7c5-49df-ba49-2d17ce747f09 · outbound

This paper cites Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions.

Machine-learning approaches to accelerating lattice simulations Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions

Reference 38

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Observation b2d05ec3-0f6d-4221-baa9-da03de5f66f9 · outbound

This paper cites Flow-based sampling in the lattice Schwinger model at criticality.

Machine-learning approaches to accelerating lattice simulations Flow-based sampling in the lattice Schwinger model at criticality

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.633074Z

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Observation b4da86b9-fe3e-44e7-af36-a84e9ee480fe · outbound

This paper cites Normalizing flows for lattice gauge theory in arbitrary space-time dimension.

Machine-learning approaches to accelerating lattice simulations Normalizing flows for lattice gauge theory in arbitrary space-time dimension

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.748495Z

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Observation 864d1f37-14e9-48df-b167-d38e1dc36829 · outbound

This paper cites Learning Trivializing Gradient Flows for Lattice Gauge Theories.

Machine-learning approaches to accelerating lattice simulations Learning Trivializing Gradient Flows for Lattice Gauge Theories

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.779670Z

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Observation 06061004-ac90-4b17-9af2-3a73db13548b · outbound

This paper cites Lattice Scalar Field Theory At Complex Coupling.

Machine-learning approaches to accelerating lattice simulations Lattice Scalar Field Theory At Complex Coupling

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:43.882999Z

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Observation 3fc7533f-48be-4207-b3a4-8dfdea709b84 · outbound

This paper cites Single Particle Spectrum of Doped $\mathrm{C}_{20}\mathrm{H}_{12}$-Perylene.

Machine-learning approaches to accelerating lattice simulations Single Particle Spectrum of Doped $\mathrm{C}_{20}\mathrm{H}_{12}$-Perylene

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.791825Z

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Observation 20ea1b67-b891-4c54-b4ed-9647d5ef2bab · outbound

This paper cites Complex Paths Around The Sign Problem.

Machine-learning approaches to accelerating lattice simulations Complex Paths Around The Sign Problem

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.796898Z

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Observation 30dfe68e-2796-417a-afb3-0bf96d7fdd12 · outbound

This paper cites Analytic Continuation Of Chern-Simons Theory.

Machine-learning approaches to accelerating lattice simulations Analytic Continuation Of Chern-Simons Theory

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.802316Z

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Observation 57701acf-affe-4936-9170-337756548358 · outbound

This paper cites High density QCD on a Lefschetz thimble?.

Machine-learning approaches to accelerating lattice simulations High density QCD on a Lefschetz thimble?

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.807393Z

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Observation 1bf140a0-0c09-4767-82e2-270cfc285808 · outbound

This paper cites Deep Learning Beyond Lefschetz Thimbles.

Machine-learning approaches to accelerating lattice simulations Deep Learning Beyond Lefschetz Thimbles

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.811950Z

Source-reported events for the cited work

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Observation af25e423-783f-4a35-872b-a6ae87ac322a · outbound

This paper cites Toward solving the sign problem with path optimization method.

Machine-learning approaches to accelerating lattice simulations Toward solving the sign problem with path optimization method

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.817496Z

Source-reported events for the cited work

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Observation 43016730-0c2f-4ac8-bbcf-cd8c99f0ebb8 · outbound

This paper cites Finite-Density Monte Carlo Calculations on Sign-Optimized Manifolds.

Machine-learning approaches to accelerating lattice simulations Finite-Density Monte Carlo Calculations on Sign-Optimized Manifolds

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.822478Z

Source-reported events for the cited work

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Observation 012a2962-e720-40fa-b711-ef1d68b13ed8 · outbound

This paper cites Fermions at Finite Density in (2+1)d with Sign-Optimized Manifolds.

Machine-learning approaches to accelerating lattice simulations Fermions at Finite Density in (2+1)d with Sign-Optimized Manifolds

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.828285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf51bdf2-b882-41b3-b860-17e2724e2827 · outbound

This paper cites Fermionic Sign Problem Minimization by Constant Path Integral Contour Shifts.

Machine-learning approaches to accelerating lattice simulations Fermionic Sign Problem Minimization by Constant Path Integral Contour Shifts

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.833265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d14990e-161b-42e6-b85d-802cccdda94f · outbound

This paper cites PT-symmetric quantum field theory in D dimensions.

Machine-learning approaches to accelerating lattice simulations PT-symmetric quantum field theory in D dimensions

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.838241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cf98a10-f5df-4b6a-90dc-67a7abd342f0 · outbound

This paper cites On the negative coupling O(N) model in 2d at high temperature.

Machine-learning approaches to accelerating lattice simulations On the negative coupling O(N) model in 2d at high temperature

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.843166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe000fdc-d651-463a-8f44-fcdf313334f7 · outbound

This paper cites Instantons, analytic continuation, and $\mathcal{PT}$-symmetric field theory.

Machine-learning approaches to accelerating lattice simulations Instantons, analytic continuation, and $\mathcal{PT}$-symmetric field theory

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.886227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 920eca59-1a6e-483e-9b26-42db6d02d816 · outbound

This paper cites Weller,Can negative bare couplings make sense? The®𝜙4 theory at large𝑁, 2310.02516.

Machine-learning approaches to accelerating lattice simulations Weller,Can negative bare couplings make sense? The®𝜙4 theory at large𝑁, 2310.02516

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-08-09T11:38:43.607958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d2f249a-b6b6-4128-a5fb-a6b6a722a952 · outbound

This paper cites Exponential reduction of the sign problem at finite density in the 2+1D XY model via contour deformations.

Machine-learning approaches to accelerating lattice simulations Exponential reduction of the sign problem at finite density in the 2+1D XY model via contour deformations

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.982445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88887cdc-83e8-4577-b6b0-eea4ddb2ec2d · outbound

This paper cites Path optimization for $U(1)$ gauge theory with complexified parameters.

Machine-learning approaches to accelerating lattice simulations Path optimization for $U(1)$ gauge theory with complexified parameters

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.086182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19ff9316-823a-4eab-940f-c179c2c7fc06 · outbound

This paper cites Sign optimization and complex saddle points in one-dimensional QCD.

Machine-learning approaches to accelerating lattice simulations Sign optimization and complex saddle points in one-dimensional QCD

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.120980Z

Source-reported events for the cited work

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Observation cc34730f-527c-4dbd-8fa1-beaa6f14a378 · outbound

This paper cites Heavy-dense QCD, sign optimization and Lefschetz thimbles.

Machine-learning approaches to accelerating lattice simulations Heavy-dense QCD, sign optimization and Lefschetz thimbles

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.125725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee902b08-a882-45c6-825c-f677c853fd1c · outbound

This paper cites Gauge invariant input to neural network for path optimization method.

Machine-learning approaches to accelerating lattice simulations Gauge invariant input to neural network for path optimization method

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.131486Z

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Observation 7c5bfe57-e5dd-474d-9e8a-6bb02e84b5f7 · outbound

This paper cites Fighting the sign problem in a chiral random matrix model with contour deformations.

Machine-learning approaches to accelerating lattice simulations Fighting the sign problem in a chiral random matrix model with contour deformations

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.137117Z

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Observation 147e4052-4df6-4d6e-9ea7-ec9e1e2152a6 · outbound

This paper cites Finite Density $QED_{1+1}$ Near Lefschetz Thimbles.

Machine-learning approaches to accelerating lattice simulations Finite Density $QED_{1+1}$ Near Lefschetz Thimbles

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:43.251774Z

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Observation 6162ef65-ec02-4021-80b2-89ae30676d69 · outbound

This paper cites Application of the path optimization method to a discrete spin system.

Machine-learning approaches to accelerating lattice simulations Application of the path optimization method to a discrete spin system

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:43.082434Z

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Observation 5b54033a-6aa7-4c27-bfd2-2a824db25fb2 · outbound

This paper cites Real-time Spin Systems from Lattice Field Theory.

Machine-learning approaches to accelerating lattice simulations Real-time Spin Systems from Lattice Field Theory

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:43.003113Z

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Observation 9a17ea40-8495-430d-ba82-4d52f6c1e7c1 · outbound

This paper cites Lefschetz Thimble Quantum Monte Carlo for Spin Systems.

Machine-learning approaches to accelerating lattice simulations Lefschetz Thimble Quantum Monte Carlo for Spin Systems

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:42.977843Z

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Observation c8094a0e-ce9a-470b-92ef-abd1171cda1f · outbound

This paper cites Schwinger-Keldysh on the lattice: a faster algorithm and its application to field theory.

Machine-learning approaches to accelerating lattice simulations Schwinger-Keldysh on the lattice: a faster algorithm and its application to field theory

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.164375Z

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Observation 2c40ee20-b707-4c47-87b2-55505bdfaa04 · outbound

This paper cites Monte Carlo study of real time dynamics.

Machine-learning approaches to accelerating lattice simulations Monte Carlo study of real time dynamics

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.169453Z

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Observation 5b1220aa-c0d7-4198-8099-0498155164d8 · outbound

This paper cites Normalizing Flows and the Real-Time Sign Problem.

Machine-learning approaches to accelerating lattice simulations Normalizing Flows and the Real-Time Sign Problem

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.175159Z

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Observation 257762cc-19a6-4442-9f54-99056f79205e · outbound

This paper cites Real-time lattice gauge theory actions: unitarity, convergence, and path integral contour deformations.

Machine-learning approaches to accelerating lattice simulations Real-time lattice gauge theory actions: unitarity, convergence, and path integral contour deformations

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:42.902183Z

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Observation 1f2741ee-0872-40aa-9cf5-f9c2169dac74 · outbound

This paper cites Path integral contour deformations for observables in $SU(N)$ gauge theory.

Machine-learning approaches to accelerating lattice simulations Path integral contour deformations for observables in $SU(N)$ gauge theory

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.186310Z

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Observation 2ea0b822-94b8-4b55-a0de-0c39fe1d91f2 · outbound

This paper cites Path integral contour deformations for noisy observables.

Machine-learning approaches to accelerating lattice simulations Path integral contour deformations for noisy observables

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.192428Z

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Observation 9cbb362b-d4a7-47ab-a2bd-f2dec312f24c · outbound

This paper cites Mitigating Green's function Monte Carlo signal-to-noise problems using contour deformations.

Machine-learning approaches to accelerating lattice simulations Mitigating Green's function Monte Carlo signal-to-noise problems using contour deformations

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.197481Z

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Observation 4bfe67e6-6b70-456d-b811-59ea322e0db9 · outbound

This paper cites Contour deformations for non-holomorphic actions.

Machine-learning approaches to accelerating lattice simulations Contour deformations for non-holomorphic actions

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:42.823662Z

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Observation a6585d81-912e-4f78-83fc-ad6315cb1f92 · outbound

This paper cites Convex optimization and contour deformations.

Machine-learning approaches to accelerating lattice simulations Convex optimization and contour deformations

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.289528Z

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Observation 73fa517c-8a23-479f-9665-d64a31d9956a · outbound

This paper cites Applications of flow models to the generation of correlated lattice QCD ensembles.

Machine-learning approaches to accelerating lattice simulations Applications of flow models to the generation of correlated lattice QCD ensembles

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.341530Z

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Observation c38e9210-8da7-4e02-87dc-5425d8384a0e · outbound

This paper cites Deep Learning of Fermion Sign Fluctuations.

Machine-learning approaches to accelerating lattice simulations Deep Learning of Fermion Sign Fluctuations

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.347274Z

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Observation c538fdb4-4800-4787-be4a-975a365f7e70 · outbound

This paper cites Perturbative Removal of a Sign Problem.

Machine-learning approaches to accelerating lattice simulations Perturbative Removal of a Sign Problem

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:42.752442Z

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Observation 17dbd37e-f2f5-4ad3-a9ca-62f3a3cad605 · outbound

This paper cites Mitigating a discrete sign problem with extreme learning machines.

Machine-learning approaches to accelerating lattice simulations Mitigating a discrete sign problem with extreme learning machines

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.357356Z

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Observation 1a359337-50d6-4af0-a1d4-83a41c6fb96a · outbound

This paper cites Control variates for lattice field theory.

Machine-learning approaches to accelerating lattice simulations Control variates for lattice field theory

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.362234Z

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Observation adf60721-d343-4c17-b3b8-8401fa6fb4db · outbound

This paper cites Leveraging neural control variates for enhanced precision in lattice field theory.

Machine-learning approaches to accelerating lattice simulations Leveraging neural control variates for enhanced precision in lattice field theory

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.367507Z

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Observation f528f7a3-d5ba-441c-8fea-a6cacb77e1b4 · outbound

This paper cites Schwinger-Dyson control variates for lattice fermions.

Machine-learning approaches to accelerating lattice simulations Schwinger-Dyson control variates for lattice fermions

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.373283Z

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Observation 7dea4378-0f4e-4309-a230-99cf21707a2f · outbound

This paper cites Machine-Learning Prediction for Quasi-PDF Matrix Elements.

Machine-learning approaches to accelerating lattice simulations Machine-Learning Prediction for Quasi-PDF Matrix Elements

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:42.662287Z

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Observation 3f41b629-2d4d-40d3-b327-758e31777808 · outbound

This paper cites Machine Learning Estimators for Lattice QCD Observables.

Machine-learning approaches to accelerating lattice simulations Machine Learning Estimators for Lattice QCD Observables

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.383295Z

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Observation 1773abaf-12b9-48af-9150-9628c720e7c7 · outbound

This paper cites Machine learning mapping of lattice correlated data.

Machine-learning approaches to accelerating lattice simulations Machine learning mapping of lattice correlated data

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:42.535444Z

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Observation b5f47b0a-0df9-4fd9-a17a-aa482d52c589 · outbound

This paper cites Introduction to Normalizing Flows for Lattice Field Theory.

Machine-learning approaches to accelerating lattice simulations Introduction to Normalizing Flows for Lattice Field Theory

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:42.393482Z

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Observation 2f48822b-8a92-4f1a-9cfb-71e32b81403c · outbound

This paper cites GomalizingFlow.jl: A Julia package for Flow-based sampling algorithm for lattice field theory.

Machine-learning approaches to accelerating lattice simulations GomalizingFlow.jl: A Julia package for Flow-based sampling algorithm for lattice field theory

Reference 86

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:38:42.451504Z

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Observation 81d45db1-560a-4318-908d-5f4d453f7f1d · outbound

This paper cites Nicoli, C.J.

Machine-learning approaches to accelerating lattice simulations Nicoli, C.J

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:38:44.841503Z

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Pith citing papers

Observation d54dbd5e-0ade-4aa3-b8ea-f343223a003d · inbound

Machine learning for four-dimensional SU(3) lattice gauge theories cites this paper.

Machine learning for four-dimensional SU(3) lattice gauge theories Machine-learning approaches to accelerating lattice simulations

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:25:29.410181Z

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Observation 11eea816-a650-4c1a-ac66-f9da4c1e2b73 · inbound

Testing machine-learned distributions against Monte Carlo data for the QCD chiral phase transition cites this paper.

Testing machine-learned distributions against Monte Carlo data for the QCD chiral phase transition Machine-learning approaches to accelerating lattice simulations

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:10:58.616361Z

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