Paper Citation Record · LEDGER

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory

As of 4 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2510.06508.

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

pith.paper-citation-record.v1
2510.06508 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T09:07:29.463814Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-15T14:46:33.294601Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

55 of 55 outbound references displayed

verified exact9
verified fuzzy25
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e0d294a4-d9a1-4b6d-bbe7-b3c51ef48ff4 · outbound

This paper cites Ziatdinov, O.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Ziatdinov, O

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.831072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 02b3f0d5-640f-4549-87f5-4227b8b0d9da · outbound

This paper cites Gordon, P.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Gordon, P

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.851374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c6fa6123-bd51-462b-a116-250838c47499 · outbound

This paper cites Borodinov, S.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Borodinov, S

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.864679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 412d799c-aa75-46ad-a12b-3b0c1774138c · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.870023Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9a4c3b5e-3ed8-473b-875a-bec8f380c5e8 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.845946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 771eb7f2-0296-4873-a37e-675ed6b1fff1 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.839794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8ed445a9-5076-4a45-b6cb-c3fa56aa3a37 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.836984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e1be1a5f-17a7-49a5-b3a5-825b73ed3a69 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.822431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 507ad490-c6f6-438b-b96c-46f642ff0906 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.827785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c9c392de-aaef-42c2-8f90-1820bc82a872 · outbound

This paper cites CktGNN: Circuit Graph Neural Network for Electronic Design Automation.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory CktGNN: Circuit Graph Neural Network for Electronic Design Automation

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:11:09.825790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 02295ef7-1e71-4d60-acc7-7545dafbf3b5 · outbound

This paper cites Koch-Janusz and Z.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Koch-Janusz and Z

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.813427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 19205b65-bb85-48d0-949d-59e55e77003d · outbound

This paper cites Li and L.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Li and L

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.735616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8ec13de5-8aed-4f04-a39d-7f607d9b6e45 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.728841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2f2f39e7-4409-47ed-b4e1-4280722b7d6d · outbound

This paper cites Hu, S.-H.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Hu, S.-H

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.810123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d69dcc89-0ed4-4082-a87c-3ea09ea31bc0 · outbound

This paper cites Chung and Y.-J.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Chung and Y.-J

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.816547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b0a565db-f447-4dcb-8db3-c2892ecff1e6 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.806974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b5f29bcb-09ca-44b7-a33e-665d33543440 · outbound

This paper cites Giataganas, C.-Y.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Giataganas, C.-Y

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.732298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2ab32061-3f55-419e-b273-5d9dbdde28ba · outbound

This paper cites Bachtis, G.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Bachtis, G

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.800640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e1bd5bf9-c6e1-41d8-9354-b0fbe034afcf · outbound

This paper cites Sheshmani, Y.-Z.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Sheshmani, Y.-Z

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.803862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 48b72dd5-b407-4e73-9b2b-a7747f57eee7 · outbound

This paper cites Di Sante, M.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Di Sante, M

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.819535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6028e513-2665-434a-9bb1-e0331ad965c0 · outbound

This paper cites Ueda and M.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Ueda and M

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.825101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 59aa0b32-e790-4240-acd0-e7902486fb4d · outbound

This paper cites Hou and Y.-Z.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Hou and Y.-Z

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.833947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 913bbc77-f2f1-49d6-9514-3496190e2a90 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.842887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 07974e12-7fa9-4ba1-98a8-906a6f02b7d7 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.791640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c21cc82c-72f4-4800-abae-60af065eecf2 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.775438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 72a86c0e-39b3-494b-ae18-3bea71edecf5 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.769208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1f3cc297-46a0-41c8-a90c-256a9f380f0e · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.862076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6ecf2bcd-68ad-45be-b8fb-95f5cebb622d · outbound

This paper cites Exact holographic mapping and emergent space-time geometry.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Exact holographic mapping and emergent space-time geometry

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:11:09.848801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9ba8926d-4eb9-494b-955e-13d5170237b5 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.854206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 466efab4-0338-4af8-b38f-d8347912a3f0 · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.856918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 54b71205-3f6b-4178-9169-ed1e07cacdca · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.859493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ca8e2243-8e0c-499b-ac75-a60b947490da · outbound

This paper cites Papamakarios, E.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Papamakarios, E

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.867235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 934da559-545e-4b1d-938d-09b563a8aecb · outbound

This paper cites Kobyzev, S.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Kobyzev, S

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.848616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d7290441-0b35-41fa-aca1-08a9f7dde6a5 · outbound

This paper cites Density estimation using Real NVP.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Density estimation using Real NVP

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:11:09.853754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 265d624b-d885-4a39-a707-1d5d857a08fa · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.779006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e40fd09a-88d7-4f1e-84b0-3e26f8a4263a · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.788262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2db5ea78-0d93-4dd8-8d8f-b04990cdddc3 · outbound

This paper cites Loinaz and R.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Loinaz and R

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.759632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 112fa81e-79bb-4e4e-8b92-2e085eedf336 · outbound

This paper cites Sugihara, Density matrix renormalization group in a two-dimensionalλϕ 4 hamiltonian lattice model, Journal of High Energy Physics2004, 007 (2004).

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Sugihara, Density matrix renormalization group in a two-dimensionalλϕ 4 hamiltonian lattice model, Journal of High Energy Physics2004, 007 (2004)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.782247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0bca90c3-7ca4-4fce-b0c3-e236cea8effe · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.772482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a6171823-b321-4643-9376-de19e64a7903 · outbound

This paper cites Schaich and W.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Schaich and W

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.753292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b39df8f8-8d35-4078-a7e5-3866757339b3 · outbound

This paper cites Milsted, J.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Milsted, J

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.756641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3542f677-b14e-4e06-ae37-4cd0db94866d · outbound

This paper cites Rychkov and L.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Rychkov and L

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.797615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ee886cbf-9983-450c-9c5e-7c78720994dd · outbound

This paper cites Serone, G.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Serone, G

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.762856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bee6c247-e5a5-43f7-bb7c-9f8178703160 · outbound

This paper cites Delcamp and A.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Delcamp and A

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.794525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b6fcbd6d-fea6-4ffa-bdfc-634e80134958 · outbound

This paper cites Shankar,Quantum Field Theory and Condensed Matter: An Introduction(Cambridge University Press, 2017).

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Shankar,Quantum Field Theory and Condensed Matter: An Introduction(Cambridge University Press, 2017)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.746538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a0cd62c5-5e77-49e5-a943-22617f6799e1 · outbound

This paper cites Kingma and J.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Kingma and J

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.750030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation de368df8-ba30-463f-b3fb-8ab34587ef2c · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.738991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a23e9a82-cb46-41d8-927c-0dc03ee95f0d · outbound

This paper cites an unresolved cited work.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:11:10.785448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7d16bdb0-c5ea-41f1-8f6d-8ac1d88a2845 · outbound

This paper cites Cardy,Scaling and Renormalization in Statistical Physics, Cambridge Lecture Notes in Physics (Cambridge University Press, Cambridge, 1996).

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Cardy,Scaling and Renormalization in Statistical Physics, Cambridge Lecture Notes in Physics (Cambridge University Press, Cambridge, 1996)

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:11:10.742913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 24947820-f67c-4a9f-9fba-0a42f47bd7ce · outbound

This paper cites Neural Ordinary Differential Equations.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Neural Ordinary Differential Equations

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:11:09.830134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f214b3ac-6d3c-4815-a76c-5588bfead7e1 · outbound

This paper cites FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:11:09.843921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c5d4df68-daf2-472f-8cd7-54303ef25d8a · outbound

This paper cites Group Equivariant Convolutional Networks.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory Group Equivariant Convolutional Networks

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:11:09.863219Z

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Observation b57f1e0e-d740-42b3-8275-879356fe1b89 · outbound

This paper cites On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:11:09.834965Z

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Observation 51bb13e4-cb5e-4af5-b9e6-bbcf44d842da · outbound

This paper cites 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:11:09.858514Z

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Observation c8fcb9c8-49cf-43f7-ad9c-079ed206a035 · outbound

This paper cites A General Theory of Equivariant CNNs on Homogeneous Spaces.

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory A General Theory of Equivariant CNNs on Homogeneous Spaces

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:11:09.839573Z

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Pith citing papers

Observation d56c27a8-3d09-4d7b-9c8b-6372e81fd1b1 · inbound

Uncertainty and Autarky: Cooperative Game Theory for Stable Local Energy Market Partitioning cites this paper.

Uncertainty and Autarky: Cooperative Game Theory for Stable Local Energy Market Partitioning Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory

Reference 56

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