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Paper Citation Record · LEDGER

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium

As of 8 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 0 inbound Pith citation observations for arXiv:2607.21152.

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

pith.paper-citation-record.v1
2607.21152 v1

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T08:23:32.903141Z

measured 72 of 72 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

72 of 72 outbound references displayed

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External citation measurements

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Outbound references

Observation 19ac7025-8819-4092-8e29-fd4380e3ffc8 · outbound

This paper cites Annual Review of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Annual Review of Fluid Mechanics , volume=

Reference 1

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Observation 14d4a534-e7d4-4391-a7bf-120d306b0094 · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 2

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Observation 860f9531-45d0-4c1c-a086-1e8a36426582 · outbound

This paper cites Physical Review Letters , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physical Review Letters , volume=

Reference 3

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Observation 9c35ebb7-2cc0-41a8-817c-d0f8165cf69a · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 4

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Observation 41dd029d-0331-430f-bac7-652cfe10ba51 · outbound

This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 5

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Observation 7df706be-f96e-43d3-b5cc-64ac34f90a3d · outbound

This paper cites Fluid Dynamics Research , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Fluid Dynamics Research , volume=

Reference 6

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Observation c881659e-014c-42de-aeae-41682aaae25b · outbound

This paper cites Physical Review E , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physical Review E , volume=

Reference 7

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Observation 0e81e607-7088-4457-8cc3-5ebe1eca0e1e · outbound

This paper cites Proceedings of the Royal Society of London.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Proceedings of the Royal Society of London

Reference 8

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Observation 152a95b1-1a1e-4aac-83a0-0e7bd899099f · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 9

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Observation 0ba6df6b-7aa2-4809-aac8-b0e8d5998e2f · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 10

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Observation 2db47e01-7f2a-44f7-aeff-dbaf3bfe7fae · outbound

This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 11

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Observation 4a5e9002-983e-48d6-b403-05c1744a7d06 · outbound

This paper cites Nature Physics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Nature Physics , volume=

Reference 12

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Observation 3352e359-cee8-402e-b189-5b4a0f1aa801 · outbound

This paper cites Journal de Physique , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal de Physique , volume=

Reference 13

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Observation 0ca1292c-5fbf-4971-950c-f7e64833be36 · outbound

This paper cites Physics of Fluids A: Fluid Dynamics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids A: Fluid Dynamics , volume=

Reference 14

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This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 15

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Observation 985806b6-8ce5-464a-bf4b-632289be7234 · outbound

This paper cites Theoretical and Computational Fluid Dynamics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Theoretical and Computational Fluid Dynamics , volume=

Reference 16

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Observation 8a2ece7b-28a1-4e0e-959f-67eb96b39b0a · outbound

This paper cites Physical Review Letters , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physical Review Letters , volume=

Reference 17

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Observation b752af6a-097d-4ff8-8041-962f3553eb30 · outbound

This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 18

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Observation 22b9d520-9a8f-4331-a73a-3802cf8a11a0 · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 19

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This paper cites Progress in Energy and Combustion Science , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Progress in Energy and Combustion Science , volume=

Reference 20

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Observation 831d2e44-115b-47bc-b32d-13ffbfb11724 · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 21

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Observation 9bf611ac-24b6-42fb-98ee-ee2d3b554f44 · outbound

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A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids A: Fluid Dynamics , volume=

Reference 22

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This paper cites Physical Review Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physical Review Fluids , volume=

Reference 23

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This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 24

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Observation f9658abf-b8c4-4939-ace0-e21f70b5db5c · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 25

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A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids A: Fluid Dynamics , volume=

Reference 26

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A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of fluids , volume=

Reference 27

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A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 28

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This paper cites Machine Learning for Fluid Mechanics.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Machine Learning for Fluid Mechanics

Reference 29

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A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physical Review Fluids , volume=

Reference 30

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This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 31

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A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Proceedings of the 2007 ACM/IEEE Conference on Supercomputing , pages=

Reference 32

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A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Turbulence , volume=

Reference 33

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A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Turbulence , volume=

Reference 34

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A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Science , volume=

Reference 35

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A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physica D: Nonlinear Phenomena , volume=

Reference 36

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A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium 2016 , publisher=

Reference 37

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A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium 2000 , pages=

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source=arxiv_source observed=2026-08-01T08:23:29.263001Z digest=sha256:3249c483d1942ca62231c4ce8a66597703802300c44362343667712674c833f5

Observation ff4c2d3d-f4a2-439e-ac57-182c7faa5331 · outbound

This paper cites Physical Review E , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physical Review E , volume=

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source=arxiv_source observed=2026-08-01T08:23:29.348730Z digest=sha256:da540945d2a180d56422a186de68271b6d799bbacb0c22b6825e69a2dd6783e3

Observation 400529b5-aa4f-4d44-aa47-807012179ed0 · outbound

This paper cites Journal of Turbulence , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Turbulence , volume=

Reference 40

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source=arxiv_source observed=2026-08-01T08:23:29.445864Z digest=sha256:9e45ab48eec8929839d996ccac7f320e2dd1e0e786c1e0db98769bc0e15f6210

Observation e9808d7b-9cdf-4f6b-a0b8-4d4df6077378 · outbound

This paper cites 1995 , publisher=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium 1995 , publisher=

Reference 41

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source=arxiv_source observed=2026-08-01T08:23:29.534897Z digest=sha256:cdb498846a33f0ace76ae16397ced6de28bfe7ee811f380476e06797af00c2e0

Observation 12258ea4-d701-4f10-a321-371a938550e1 · outbound

This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 42

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source=arxiv_source observed=2026-08-01T08:23:29.623602Z digest=sha256:44fa0f65182ab67dc4b55d1dd20031a0afc365a5a6c50bae710023552acabf0d

Observation b986af63-b08a-4a90-bd32-41840d60ef47 · outbound

This paper cites Journal of Computational Physics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Computational Physics , volume=

Reference 43

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source=arxiv_source observed=2026-08-01T08:23:29.701629Z digest=sha256:92ef1e051ac0d624f54c0c476a71fee75af50a6ef926529c233ae6e775b70ebb

Observation 382b9873-a4c6-40a2-8ac1-f32fc8959169 · outbound

This paper cites The Journal of Machine Learning Research , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium The Journal of Machine Learning Research , volume=

Reference 44

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source=arxiv_source observed=2026-08-01T08:23:29.795842Z digest=sha256:5d916eafdbaa0b47e2b5e08f22d7d3ef1f76a79a55848b6fe7e9e6c57ab931c9

Observation a33c0ac7-8b31-4c0d-98ca-33af8db139a2 · outbound

This paper cites Journal of Computational Physics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Computational Physics , volume=

Reference 45

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source=arxiv_source observed=2026-08-01T08:23:29.915117Z digest=sha256:8e7f9ddd6300aefbc8409ae723f8b2eeb80c5182e93d15546c03c57c712c6fba

Observation 5aac4ff8-f09f-4f41-86e6-92366982e307 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Advances in Neural Information Processing Systems , volume=

Reference 46

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source=arxiv_source observed=2026-08-01T08:23:30.007114Z digest=sha256:5d48a62ed65dbbf60bdb98c4f9e61a4d117bb97d834d6c2c490351c3e84571d3

Observation 71a1e3fb-dcff-4ae5-bd71-228674e2074f · outbound

This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 47

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source=arxiv_source observed=2026-08-01T08:23:30.075450Z digest=sha256:6cc780447d5488fe9e215a91094ae55a5cc15ecdd2e40771c52e02771a9734b0

Observation 1b8e5ae8-eab7-47b2-92d3-3dcf5f7ca9e3 · outbound

This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 48

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source=arxiv_source observed=2026-08-01T08:23:30.141272Z digest=sha256:8719c1f447ce0f23b5bc1d1cdddcc527d94790c58fac9853a5448e7c3475d97a

Observation 17179c52-3849-48e1-9b4e-2f760ff40438 · outbound

This paper cites Experimental Thermal and Fluid Science , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Experimental Thermal and Fluid Science , volume=

Reference 49

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source=arxiv_source observed=2026-08-01T08:23:30.269542Z digest=sha256:9cf02501860dec27ab886b6520df343c6cb4d8bdccd77ffac0202d45e8ea1846

Observation 6cf56fd6-102b-42cf-bf20-e6383353f615 · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 50

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source=arxiv_source observed=2026-08-01T08:23:30.403309Z digest=sha256:294bd43ba3f8d3a92fb94b251ed6707da91bd99173d23a878abdf5e96c220415

Observation 3d389bb2-5faa-4ac8-8bd2-a657189d8013 · outbound

This paper cites Annual Review of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Annual Review of Fluid Mechanics , volume=

Reference 51

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source=arxiv_source observed=2026-08-01T08:23:30.486022Z digest=sha256:25d13d4b7f722b42fb645b385e6621d56c5caa5b04d2296dc3ab844f5c9ba0f3

Observation 1063ecd8-23c6-4cf2-b9f7-5b3f2662373b · outbound

This paper cites Journal of Computational Physics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Computational Physics , volume=

Reference 52

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source=arxiv_source observed=2026-08-01T08:23:30.570814Z digest=sha256:c4f63104c689b7c6cd9e33e1265718502d4b0b3f2a3056e0a675189b2ce90fa5

Observation 92415a8c-d801-4255-b1d0-977810209aa5 · outbound

This paper cites Physics Reports , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics Reports , volume=

Reference 53

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source=arxiv_source observed=2026-08-01T08:23:30.651375Z digest=sha256:ed744b9e07063e89328029580939bfc7961adfd7404c9558e17b5cf868ab9ee9

Observation c0529a5a-12f6-4f9c-be52-03abcc8509b9 · outbound

This paper cites Physics of Life Reviews , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Life Reviews , volume=

Reference 54

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source=arxiv_source observed=2026-08-01T08:23:30.738031Z digest=sha256:c33423c3b2309fd5219c0e98a12d17f5778b4c807eecc0030e0cceff0a9921ab

Observation 89db60e8-d606-4374-8da1-ceca6cfb6b9c · outbound

This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 55

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source=arxiv_source observed=2026-08-01T08:23:30.828853Z digest=sha256:f743856bc196e64da82abb94eb531d255404b9f04e6dbf484b7dd35208d20728

Observation f780516a-6636-49f3-b022-b955c6b0fa1f · outbound

This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 56

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source=arxiv_source observed=2026-08-01T08:23:30.916125Z digest=sha256:01af2e46059864b990df40306679d462527299b5777e67a5b40441be47ca479c

Observation 9ee68feb-a61c-4694-9c45-615c953f22fe · outbound

This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 57

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source=arxiv_source observed=2026-08-01T08:23:31.047400Z digest=sha256:610119a359bda1674a2adb57f0d5527180271bf7ff7df0c90ad934fbd0261b15

Observation bc4449c7-f0ef-42cf-b1e5-d7d178b1040e · outbound

This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 58

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source=arxiv_source observed=2026-08-01T08:23:31.189426Z digest=sha256:38c06e6abeac1a21f734a8995e91a1a172081c29cb7a1e11ad41e2c6edfbc930

Observation a125363f-bd13-48f5-bd9b-4cf881ac794c · outbound

This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 59

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source=arxiv_source observed=2026-08-01T08:23:31.304337Z digest=sha256:6d0073190b206323ee33815ffb16ce3d7d796556eab8199709162d3646a1ca3f

Observation e1a9915b-fc19-4334-bc81-c2c606ca398d · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 60

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source=arxiv_source observed=2026-08-01T08:23:31.429540Z digest=sha256:7f1624658854ddac0f0764b83ee2dfbe64a11c9dd8784ada31e13bde3397e7d1

Observation 3097f746-4c83-47fc-b752-b67116e7290b · outbound

This paper cites Theoretical and Computational Fluid Dynamics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Theoretical and Computational Fluid Dynamics , volume=

Reference 61

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source=arxiv_source observed=2026-08-01T08:23:31.601633Z digest=sha256:29ce00c8f3ae5ff48d654403f0a6b459878a6456ee11cd8f0421480c7817afcf

Observation 2fa6d449-9294-43ff-874d-6ddde46ce684 · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 62

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source=arxiv_source observed=2026-08-01T08:23:31.693819Z digest=sha256:219a068fad2cc2330e2d3c1c62699731798cb1d315a00a361fdccce288ad6b0c

Observation fc93a634-dc4c-4631-a3e9-3af68e33872b · outbound

This paper cites AIAA Journal , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium AIAA Journal , volume=

Reference 63

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source=arxiv_source observed=2026-08-01T08:23:31.832761Z digest=sha256:fc9da247aa67225ee5460b48ac4fb79661d7c52ebdf8b9f620500e671cc888e8

Observation 30729293-885d-4dfb-897d-3ae38feac7c7 · outbound

This paper cites Archive for rational mechanics and analysis , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Archive for rational mechanics and analysis , volume=

Reference 64

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source=arxiv_source observed=2026-08-01T08:23:32.014964Z digest=sha256:526bdfd9d0d3f4d7518bde3759c9790984aa6203ab179e285eefc5612833a0a5

Observation 3ff347de-bbde-45cd-ab2b-31be833e64ca · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 65

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source=arxiv_source observed=2026-08-01T08:23:32.167374Z digest=sha256:3c29421c1e74c137257ab0c75dbc318ebe138b2fa53b0cda06605c990f2fb107

Observation 00b05619-be23-4140-b83a-08602b1fb7eb · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 66

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source=arxiv_source observed=2026-08-01T08:23:32.278307Z digest=sha256:46a1da7b57331ecb43721230a0d4106473eb7d009a80883058129922808a3c23

Observation 846e253e-e76c-49b7-aaad-cc8571fc3efc · outbound

This paper cites 1966 , publisher=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium 1966 , publisher=

Reference 67

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source=arxiv_source observed=2026-08-01T08:23:32.426795Z digest=sha256:b6802a3a62797207efa86f536745e0e50ce9fb2259df1309b1c038fef6a0e02f

Observation 5b5a88d5-2626-4be9-92b2-1d8bb5be0ea6 · outbound

This paper cites Journal of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Journal of Fluid Mechanics , volume=

Reference 68

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no resolver link, observed 2026-08-01T08:23:32.532061Z

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source=arxiv_source observed=2026-08-01T08:23:32.532061Z digest=sha256:dc1abe3e75ef90075be9a7eb8aae415b483179a8d1f378a30b1f91403cb44c53

Observation 4c31af57-a4e0-4f45-848f-8de47d4ee32c · outbound

This paper cites Physics of Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids , volume=

Reference 69

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source=arxiv_source observed=2026-08-01T08:23:32.628397Z digest=sha256:b37d3b6435a17009ed6cfd54928d3a6cf1652f95a2a8198b0a23613b64972c0d

Observation 264f5464-d922-4545-8f7d-33b1fea61d91 · outbound

This paper cites Physics of Fluids A: Fluid Dynamics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physics of Fluids A: Fluid Dynamics , volume=

Reference 70

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source=arxiv_source observed=2026-08-01T08:23:32.719840Z digest=sha256:dacea1165b6c084995a38b9dfaaecb2d74200b8627af6ec800cdc74d2825f5ae

Observation 2ae3cc35-fe78-462f-8fb2-72942cda8fe7 · outbound

This paper cites Physical Review Fluids , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Physical Review Fluids , volume=

Reference 71

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source=arxiv_source observed=2026-08-01T08:23:32.829452Z digest=sha256:b6cd1c7e8f9a61d18fa4299fbd08c5c30b72868ca6c2f167401210ef82362a35

Observation 8663a667-e433-4ec8-af95-30dc0c416c5d · outbound

This paper cites Annual Review of Fluid Mechanics , volume=.

A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium Annual Review of Fluid Mechanics , volume=

Reference 72

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source=arxiv_source observed=2026-08-01T08:23:32.903141Z digest=sha256:85d72fd7daba12ad4a2275704f6424aa5d4bab2cf2ab8447d04a49e337d55c6d

Pith citing papers

No inbound Pith citation observations are available.