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Paper Citation Record · LEDGER

Symplectic Recurrent Neural Networks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:1909.13334.

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

pith.paper-citation-record.v1
1909.13334 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:29:46.455649Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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  • malformed identifier0
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External citation measurements

65
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8b740734-ad56-408a-a945-5585411066b5 · inbound

Learning long range dependencies through time reversal symmetry breaking cites this paper.

Learning long range dependencies through time reversal symmetry breaking Symplectic Recurrent Neural Networks

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T10:29:46.455649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:29:46.455649Z digest=sha256:c7413567d02865c29829c81676baf388995d141695b7823823e6ef5d8edf692f

Observation 4fb3f704-2134-4316-8222-0b4d78c4a181 · inbound

Chaoticus: a parallel approach to the computation of chaos indicators cites this paper.

Chaoticus: a parallel approach to the computation of chaos indicators Symplectic Recurrent Neural Networks

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-06T21:15:07.546632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:15:07.546632Z digest=sha256:01aba9d2c0c60f749d672b256d392fba00cef8a9f0faa8f84ac79d8b88443851

Observation 88286c98-d1a9-44e3-af32-ec8d8fa658c5 · inbound

Artifacts of Numerical Integration in Learning Dynamical Systems cites this paper.

Artifacts of Numerical Integration in Learning Dynamical Systems Symplectic Recurrent Neural Networks

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-19T04:37:04.200662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T04:33:46.375174Z digest=sha256:2bac2f9e0d78657546e07abcc86a0c0c6e02d926f894c58025ac09968c231dc5

Observation 367076d5-e06c-4385-9a07-a47480890c4a · inbound

Structure-Preserving Digital Twins via Conditional Neural Whitney Forms cites this paper.

Structure-Preserving Digital Twins via Conditional Neural Whitney Forms Symplectic Recurrent Neural Networks

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-05T22:33:47.599072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:33:47.599072Z digest=sha256:3de149cfd8d85dad6c3816090f1e264b4d97cf6791f475e48cdc81e4e9a6b311

Observation 7183fcbf-5abd-4f89-a2a1-c0e4e43ab2ac · inbound

Symplectic Representation of Legendre Dynamics cites this paper.

Symplectic Representation of Legendre Dynamics Symplectic Recurrent Neural Networks

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-03T14:48:00.709068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T14:48:00.709068Z digest=sha256:4e29fc36a654988a68802c9d089bfd9ffa6d72e2ef5591156b2988a5c59a1e3c

Observation 61606698-368b-4aed-8f17-b38e26cf2128 · inbound

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling cites this paper.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Symplectic Recurrent Neural Networks

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-02T19:22:12.919302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T19:22:12.919302Z digest=sha256:47172ed801b095f171a81066bbcd4f09f21e65b60ddb1231eed560e7e2c388c6

Observation dc9ce8cc-5348-49ea-a9ff-4d7c2dbaa10f · inbound

DustNET: enabling machine learning and AI models of dusty plasmas cites this paper.

DustNET: enabling machine learning and AI models of dusty plasmas Symplectic Recurrent Neural Networks

Reference 234

Resolution
verified exact
arxiv_id, observed 2026-05-15T08:59:52.878039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-15T08:59:41.316490Z digest=sha256:21c7a29e52001b3b937c78b5507177362d00eb85601dbc09b7a462b071d79228

Observation 3bc641db-e39c-403c-b7fb-4e10208b9dfe · inbound

Learning partially observed systems with neural Hamiltonian ordinary differential equations cites this paper.

Learning partially observed systems with neural Hamiltonian ordinary differential equations Symplectic Recurrent Neural Networks

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:10:22.205561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-25T05:07:49.969047Z digest=sha256:45beb309b3331008a41f4a349849cccf5525b49f08c6eb43a4f8d4217c426685

Observation 54e484fb-3d5a-4680-9470-93d6d8f158f9 · inbound

Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by Generalized Kernel Interpolation cites this paper.

Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by Generalized Kernel Interpolation Symplectic Recurrent Neural Networks

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-06-26T20:19:55.991774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T20:15:10.881335Z digest=sha256:75bf88f656d87725f62b82781bbee8fbf2cb86dd89edebe896e51f1e605bd397

Observation 6904dcea-2fd1-484e-bdcc-a6f99e7f9a29 · inbound

Symplectic Neural Networks for Learning Non-Separable Hamiltonians cites this paper.

Symplectic Neural Networks for Learning Non-Separable Hamiltonians Symplectic Recurrent Neural Networks

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-07-04T13:09:50.210761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T05:32:31.826331Z digest=sha256:55ac325e36443a496a34c6689393c8bbb55fe5d2c313da98e1e6570632fa2654