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Paper Citation Record · LEDGER

Simulating the Hubbard Model with Equivariant Normalizing Flows

As of 13 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 2 inbound Pith citation observations for arXiv:2501.07371.

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

pith.paper-citation-record.v1
2501.07371 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:48:34.852344Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:19:44.545207Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T02:11:59.284972Z

Reference resolution

39 of 39 outbound references displayed

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  • verified fuzzy0
  • unresolved35
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  • malformed identifier0
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External citation measurements

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Outbound references

Observation 3f682d95-7f94-491b-a24d-68b12183a3a2 · outbound

This paper cites Avoiding Ergodicity Problems in Lattice Discretizations of the Hubbard Model.

Simulating the Hubbard Model with Equivariant Normalizing Flows Avoiding Ergodicity Problems in Lattice Discretizations of the Hubbard Model

Reference 1

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source=pdf_text observed=2026-08-10T20:48:32.054859Z digest=sha256:74081288a6d39ef8ba9b74fb8d931cf51dc3694a0e0d63196df6cb472bb0ef67

Observation 4ad08b71-4e21-4389-8759-35e2d7f2a283 · outbound

This paper cites Overcoming Ergodicity Problems of the Hybrid Monte Carlo Method using Radial Updates.

Simulating the Hubbard Model with Equivariant Normalizing Flows Overcoming Ergodicity Problems of the Hybrid Monte Carlo Method using Radial Updates

Reference 2

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source=pdf_text observed=2026-08-10T20:48:32.160938Z digest=sha256:d16fe22a4232f33eed077bdb7817d3e8ab276334cdebd5933b339f16cdb021c8

Observation adba8f0a-8db2-4d63-918c-203e295cfdf0 · outbound

This paper cites Variational Inference with Normalizing Flows.

Simulating the Hubbard Model with Equivariant Normalizing Flows Variational Inference with Normalizing Flows

Reference 3

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source=pdf_text observed=2026-08-10T20:48:32.203698Z digest=sha256:6a6fe33884cac97a61f4c1e9e1d58a0e0829ca880a19d0c845ab40fbfcf8c17c

Observation c6924b14-ab1f-47e5-a8e3-ca6e4a8f08df · outbound

This paper cites Normalizing Flows: An Introduction and Review of Current Methods.

Simulating the Hubbard Model with Equivariant Normalizing Flows Normalizing Flows: An Introduction and Review of Current Methods

Reference 4

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source=pdf_text observed=2026-08-10T20:48:32.250802Z digest=sha256:dbb71bd9bab27d548c6305f90965e6c75fcd8d23735f67b01c60deaaa91546c9

Observation c1d837f6-ee0d-4709-b1b1-6dde1ec25850 · outbound

This paper cites Normalizing Flows for Probabilistic Modeling and Inference.

Simulating the Hubbard Model with Equivariant Normalizing Flows Normalizing Flows for Probabilistic Modeling and Inference

Reference 5

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source=pdf_text observed=2026-08-10T20:48:32.349456Z digest=sha256:40ab58416355e3c74a7fe3cc8a67ebf46d693aa0eb6c76a16ba8e842cda7053f

Observation 69f38a58-4014-485b-9152-e918292faf6d · outbound

This paper cites Pixel Recurrent Neural Networks.

Simulating the Hubbard Model with Equivariant Normalizing Flows Pixel Recurrent Neural Networks

Reference 6

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source=pdf_text observed=2026-08-10T20:48:32.421922Z digest=sha256:09ef7b863819e0538423dc16d7bbe1db691ff4c0228695ecc00f043b143e1e82

Observation e6dc4099-2730-45b8-a75b-10d9ee8cb384 · outbound

This paper cites Conditional Image Generation with PixelCNN Decoders.

Simulating the Hubbard Model with Equivariant Normalizing Flows Conditional Image Generation with PixelCNN Decoders

Reference 7

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source=pdf_text observed=2026-08-10T20:48:32.484936Z digest=sha256:5b1a9a360876dfce3351f52c5a797abf3ff446e89129e229477031c68a0b9dd4

Observation 3343ad54-3980-493f-9835-26cd1a3ab482 · outbound

This paper cites Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning.

Simulating the Hubbard Model with Equivariant Normalizing Flows Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning

Reference 8

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source=pdf_text observed=2026-08-10T20:48:32.594968Z digest=sha256:e289bc018b21ce02c17ed84059db40d2ebab3a8a8b6c779ae5b8dd7affb1e499

Observation df002e6f-fa76-4212-9d4c-4e16bd2f1e3b · outbound

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

Simulating the Hubbard Model with Equivariant Normalizing Flows Flow-based generative models for Markov chain Monte Carlo in lattice field theory

Reference 9

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source=pdf_text observed=2026-08-10T20:48:32.704905Z digest=sha256:1c1c486d77df649931a13eb63373b32e6498f7fd16850838e19d5763f73949e2

Observation a68faed3-7432-4e4d-ad06-dac4d96d43b6 · outbound

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

Simulating the Hubbard Model with Equivariant Normalizing Flows Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models

Reference 10

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source=pdf_text observed=2026-08-10T20:48:32.783736Z digest=sha256:40f0d7fcc2cb924d151240b61f0b8244171ef7a7cc3ae0ae0cc9e30b252e26f3

Observation 6f7acd5c-fd86-4e4f-b717-6ba07a994181 · outbound

This paper cites Stochastic normalizing flows as non-equilibrium transformations.

Simulating the Hubbard Model with Equivariant Normalizing Flows Stochastic normalizing flows as non-equilibrium transformations

Reference 11

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source=pdf_text observed=2026-08-10T20:48:32.844768Z digest=sha256:4d1a5b64551899e157298371e62fa9249898411f56506343ff92d24d9cddd08a

Observation 341bc7c5-49f8-4afb-9884-79f48b9f9d6f · outbound

This paper cites Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics.

Simulating the Hubbard Model with Equivariant Normalizing Flows Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics

Reference 12

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source=pdf_text observed=2026-08-10T20:48:32.924537Z digest=sha256:68adcbe81a3ad768f587c99c64b3fef459fa8e3f664418dd8699cebe9d418265

Observation e13ff2a1-62ea-4c06-8583-35d26d4d9e30 · outbound

This paper cites Solving Statistical Mechanics Using Variational Autoregressive Networks.

Simulating the Hubbard Model with Equivariant Normalizing Flows Solving Statistical Mechanics Using Variational Autoregressive Networks

Reference 13

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source=pdf_text observed=2026-08-10T20:48:32.998206Z digest=sha256:0429a4a1f01e1acf1a7e25975bf2978ebd75873686afe32b86cbaf3d77f844c2

Observation ded9d538-946f-4df0-bd3e-5205718c55d5 · outbound

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

Simulating the Hubbard Model with Equivariant Normalizing Flows Asymptotically unbiased estimation of physical observables with neural samplers

Reference 14

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source=pdf_text observed=2026-08-10T20:48:33.065066Z digest=sha256:41389f14815ab7cfc046512b1d0457df34ccdf90a04d95e85d8622623cb3a29d

Observation 4d213c26-ebdf-4518-b7ee-49792ab17d70 · outbound

This paper cites Sampling Nambu-Goto theory using Normalizing Flows.

Simulating the Hubbard Model with Equivariant Normalizing Flows Sampling Nambu-Goto theory using Normalizing Flows

Reference 15

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source=pdf_text observed=2026-08-10T20:48:33.124161Z digest=sha256:828cae6fe1b192762758c368c3d53b30f56edc0c88e53874052bc7954ae00f30

Observation 5078abde-6b3e-47c1-a7ce-44742a9417cc · outbound

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

Simulating the Hubbard Model with Equivariant Normalizing Flows Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows

Reference 16

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source=pdf_text observed=2026-08-10T20:48:33.184933Z digest=sha256:bb0544333805b113bfe2a041994e456f677eb1efe7ca28ed8506097d436dc004

Observation b5d172f1-0275-42cc-b160-51b1c2ccc7bc · outbound

This paper cites Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules.

Simulating the Hubbard Model with Equivariant Normalizing Flows Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules

Reference 17

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source=pdf_text observed=2026-08-10T20:48:33.244889Z digest=sha256:e1824079be390e1feb8ef204abbea58f3126e5e8baa67c41c396a7ab58af7a4a

Observation 326c8cbc-78aa-4f77-a468-cfbc69f4af07 · outbound

This paper cites Gebauer, M.

Simulating the Hubbard Model with Equivariant Normalizing Flows Gebauer, M

Reference 18

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source=pdf_text observed=2026-08-10T20:48:33.331035Z digest=sha256:6d8716a5d16fd38ce2d8e96b8e4ce46dc35cfd88c08be03719a01806ead00b19

Observation 1482f3d1-16dd-442f-bf0e-5302cd4a26d3 · outbound

This paper cites R\'enyi entanglement entropy of spin chain with Generative Neural Networks.

Simulating the Hubbard Model with Equivariant Normalizing Flows R\'enyi entanglement entropy of spin chain with Generative Neural Networks

Reference 19

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source=pdf_text observed=2026-08-10T20:48:33.404751Z digest=sha256:8e56be18561bcccc97b5d7b95305b8f7316548830cf2897d9944ccd8948e055d

Observation 7f8af51c-718e-4dcb-b645-b07450744c0c · outbound

This paper cites Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects.

Simulating the Hubbard Model with Equivariant Normalizing Flows Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects

Reference 20

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source=pdf_text observed=2026-08-10T20:48:33.485171Z digest=sha256:bbfa1fd182ed60e5364168d6b30e53e55e6ac43fe9cc46863aa30d826633eedd

Observation c153ebfb-4d74-484f-b6ac-0c331ceff414 · outbound

This paper cites Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse.

Simulating the Hubbard Model with Equivariant Normalizing Flows Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse

Reference 21

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local_arxiv, observed 2026-08-10T20:48:36.704754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:48:33.545348Z digest=sha256:559212757323b6c5a9553ced0e8ba5ad1a35f55286ac15e019672a22e2713e4e

Observation 981477e0-d829-42c5-8934-cc3d8db7951f · outbound

This paper cites Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories.

Simulating the Hubbard Model with Equivariant Normalizing Flows Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories

Reference 22

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source=pdf_text observed=2026-08-10T20:48:33.605527Z digest=sha256:d0b3e22b22ee5820b7c857299c22dbac3e4a837340929a15657f5481fc02cd4a

Observation 26002e02-64ad-424d-b5d6-f7e584e97480 · outbound

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

Simulating the Hubbard Model with Equivariant Normalizing Flows Equivariant flow-based sampling for lattice gauge theory

Reference 23

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source=pdf_text observed=2026-08-10T20:48:33.694867Z digest=sha256:51199867ff42eee6704945b3992415937a786948bb37b3b56e4f805cd611a183

Observation 983a0aed-15e2-4fe3-b1e9-d07083c0fd41 · outbound

This paper cites Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities.

Simulating the Hubbard Model with Equivariant Normalizing Flows Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

Reference 24

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source=pdf_text observed=2026-08-10T20:48:33.784857Z digest=sha256:0e78ef97c16a858cb3f0de3eac68e1ed1bf99360f5bc07595ee3d6a617be5fa0

Observation c5c00377-61a7-4708-a2c0-d06e058a4ee8 · outbound

This paper cites Hubbard,Electron correlations in narrow energy bands, Proceedings of the Royal Society of London.

Simulating the Hubbard Model with Equivariant Normalizing Flows Hubbard,Electron correlations in narrow energy bands, Proceedings of the Royal Society of London

Reference 25

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source=pdf_text observed=2026-08-10T20:48:33.834857Z digest=sha256:f273eea5e3a0073d1a6b8ec5ac4058eb6e596ca344a7b93f5b59406cfbafa0fc

Observation 5c1c7fee-446f-4bf6-980c-3bfa1e86e80f · outbound

This paper cites The Hubbard Model.

Simulating the Hubbard Model with Equivariant Normalizing Flows The Hubbard Model

Reference 26

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source=pdf_text observed=2026-08-10T20:48:33.884850Z digest=sha256:d79dad36d5d19cc0277808544d04397b2b22926ad33475eee6092be4e67bd4d6

Observation 9f0b297e-e8ad-4df4-b2ba-7e61b7f0203d · outbound

This paper cites Trotter,On the product of semi-groups of operators,Proceedings of the American Mathematical Society10(1959) 545.

Simulating the Hubbard Model with Equivariant Normalizing Flows Trotter,On the product of semi-groups of operators,Proceedings of the American Mathematical Society10(1959) 545

Reference 27

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source=pdf_text observed=2026-08-10T20:48:33.943888Z digest=sha256:6829e86feefb8c3d4032f98a27d5ecbe0259d84eaa0c29ba14c753d6c53958f7

Observation 5967c9a8-a2ff-4912-833d-7c7cd13d289c · outbound

This paper cites an unresolved cited work.

Simulating the Hubbard Model with Equivariant Normalizing Flows Unresolved cited work

Reference 28

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source=pdf_text observed=2026-08-10T20:48:34.052567Z digest=sha256:0fb04958f4f9de95e0f514bdd644bb86d99fc593611239da3b7e4c7feefbc4e3

Observation 593d6c42-7781-49fb-8461-0426f91be578 · outbound

This paper cites Hubbard,Calculation of Partition Functions, Phys.

Simulating the Hubbard Model with Equivariant Normalizing Flows Hubbard,Calculation of Partition Functions, Phys

Reference 29

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source=pdf_text observed=2026-08-10T20:48:34.095378Z digest=sha256:9b0aeacc3193c23a7a05b1b8e314e4951d65c6ed01ec3ae06811ba9ac7af6020

Observation 90120ba6-34c5-4be6-8859-1cd4a1f9fd0a · outbound

This paper cites Hybrid Monte Carlo simulation on the graphene hexagonal lattice.

Simulating the Hubbard Model with Equivariant Normalizing Flows Hybrid Monte Carlo simulation on the graphene hexagonal lattice

Reference 30

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verified exact
local_arxiv, observed 2026-08-10T20:48:35.775237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:48:34.163984Z digest=sha256:3ec2ea3ac77152731b53aa58bbf1adfdc08d95a2f8ee9148f34a94fb368e7b13

Observation 158691c2-4076-4e1d-893c-decf0eecb0d1 · outbound

This paper cites Monte-Carlo study of the semimetal-insulator phase transition in monolayer graphene with realistic inter-electron interaction potential.

Simulating the Hubbard Model with Equivariant Normalizing Flows Monte-Carlo study of the semimetal-insulator phase transition in monolayer graphene with realistic inter-electron interaction potential

Reference 31

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verified exact
local_arxiv, observed 2026-08-10T20:48:35.697572Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:48:34.266002Z digest=sha256:f807b5b73cb6f6a7b7894415da6307493e107edb18a87c4f3d636038f47a9fdf

Observation adda476c-6a8d-41fd-b832-ac6b8a96d965 · outbound

This paper cites Monte-Carlo simulation of the tight-binding model of graphene with partially screened Coulomb interactions.

Simulating the Hubbard Model with Equivariant Normalizing Flows Monte-Carlo simulation of the tight-binding model of graphene with partially screened Coulomb interactions

Reference 32

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verified exact
local_arxiv, observed 2026-08-10T20:48:35.548174Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:48:34.292854Z digest=sha256:b723dc5d60f478a4f804871175cc89e0ed3f85b13215cc106f5118e9f309f532

Observation 7b6c70f0-6ce1-4877-9a4e-9e44e9c57e37 · outbound

This paper cites Quantum Monte Carlo Calculations for Carbon Nanotubes.

Simulating the Hubbard Model with Equivariant Normalizing Flows Quantum Monte Carlo Calculations for Carbon Nanotubes

Reference 33

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source=pdf_text observed=2026-08-10T20:48:34.314865Z digest=sha256:0dded7bfc9bf99d2abac00837a4634d83a80de8afeae1ea59b23cfb1770e5733

Observation 246d131d-3d70-4722-8b75-be37dcc34f2c · outbound

This paper cites Duane, A.D.

Simulating the Hubbard Model with Equivariant Normalizing Flows Duane, A.D

Reference 34

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source=pdf_text observed=2026-08-10T20:48:34.488237Z digest=sha256:1b270d328acc12ba1b3447976eb65b85f820e0d6a4ccd518deaad884a4683e97

Observation bf4cd771-46fb-44f2-982a-a9c3dc67dd21 · outbound

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

Simulating the Hubbard Model with Equivariant Normalizing Flows NICE: Non-linear Independent Components Estimation

Reference 35

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source=pdf_text observed=2026-08-10T20:48:34.544761Z digest=sha256:9359b198c0c919577d274dd63ca10557428f499b76f6d2774693855055f3ead0

Observation fa9f8bb0-2e9d-44f6-839e-1aac46773fa4 · outbound

This paper cites Density estimation using Real NVP.

Simulating the Hubbard Model with Equivariant Normalizing Flows Density estimation using Real NVP

Reference 36

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source=pdf_text observed=2026-08-10T20:48:34.571230Z digest=sha256:dc9219291932f63e16b9c8ebd9fdc08ce3a84ca907ce5c1cc673d2bbc3aedc72

Observation ec27c0f3-ee1f-41db-b620-e5373d141526 · outbound

This paper cites Kullback and R.A.

Simulating the Hubbard Model with Equivariant Normalizing Flows Kullback and R.A

Reference 37

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source=pdf_text observed=2026-08-10T20:48:34.766767Z digest=sha256:33a03ba4f726d8419dfaf88ab13e4f643063436b8405631f3d3f68ae879f1a09

Observation 66fbc065-2b22-49fe-9896-c38f05343cef · outbound

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

Simulating the Hubbard Model with Equivariant Normalizing Flows Sampling using $SU(N)$ gauge equivariant flows

Reference 38

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no resolver link, observed 2026-08-10T20:48:34.806658Z

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source=pdf_text observed=2026-08-10T20:48:34.806658Z digest=sha256:22bf2aab8a402fe9d4685a41d6269bbb6ce51c2cd71b2048b2136ff5e497c704

Observation 1e76e2a6-c79e-4875-b8c2-c1bd237364de · outbound

This paper cites Nicoli, C.J.

Simulating the Hubbard Model with Equivariant Normalizing Flows Nicoli, C.J

Reference 39

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Pith citing papers

Observation 850e0c19-6239-4a11-8fdf-0901aab8e2d0 · inbound

SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows cites this paper.

SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows Simulating the Hubbard Model with Equivariant Normalizing Flows

Reference 38

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source=pdf_text observed=2026-08-07T14:19:44.545207Z digest=sha256:f9ba9d139f0cb612d5d7c2bdd511ec59f552d8511b5b18e94a292eec35f8c4ee

Observation c2030065-863d-47ff-844d-9bc69e9820e2 · inbound

Applying the Worldvolume Hybrid Monte Carlo method to the Hubbard model away from half filling cites this paper.

Applying the Worldvolume Hybrid Monte Carlo method to the Hubbard model away from half filling Simulating the Hubbard Model with Equivariant Normalizing Flows

Reference 36

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arxiv_id, observed 2026-05-19T02:11:59.287152Z

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